{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wl3FpBgqtZ6u"
      },
      "source": [
        "# **Utils**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GsYjBsG4Jc_z"
      },
      "outputs": [],
      "source": [
        "!pip install fuzzywuzzy\n",
        "!pip install python-Levenshtein\n",
        "!pip install torchmetrics\n",
        "!pip install nltk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "GWsNQXOQJVcI"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from statistics import mean\n",
        "import pandas as pd\n",
        "from fuzzywuzzy import fuzz\n",
        "import Levenshtein\n",
        "from IPython.display import display"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "xc9fDE9MJLgP"
      },
      "outputs": [],
      "source": [
        "def calculate_fuzzy_score(reference_text, output_text):\n",
        "    fuzzy_score = fuzz.token_sort_ratio(reference_text, output_text)\n",
        "    return fuzzy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "OmRWPXfti2Oz"
      },
      "outputs": [],
      "source": [
        "def calculate_cer(reference_text, output_text):\n",
        "    distance = Levenshtein.distance(reference_text, output_text)\n",
        "    total_characters = len(reference_text)\n",
        "    cer = distance / total_characters if total_characters > 0 else float('inf')\n",
        "    return cer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "33FgDK8Vi7Yd"
      },
      "outputs": [],
      "source": [
        "def calculate_wer(reference_text, output_text):\n",
        "    reference_words = reference_text.split()\n",
        "    output_words = output_text.split()\n",
        "    distance = Levenshtein.distance(' '.join(reference_words), ' '.join(output_words))\n",
        "    total_words = len(reference_words)\n",
        "    wer = distance / total_words if total_words > 0 else float('inf')\n",
        "    return wer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Ik74uvonttmS"
      },
      "outputs": [],
      "source": [
        "import nltk\n",
        "from nltk.translate.bleu_score import sentence_bleu\n",
        "\n",
        "def calculate_bleu(reference_text, output_text):\n",
        "    reference_tokens = [reference_text.split()]\n",
        "    output_tokens = output_text.split()\n",
        "    bleu_score = sentence_bleu(reference_tokens, output_tokens)\n",
        "    return bleu_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "eTl4ZLNgt-el"
      },
      "outputs": [],
      "source": [
        "def calculate_jaccard_index(reference_text, output_text):\n",
        "    reference_set = set(reference_text)\n",
        "    output_set = set(output_text)\n",
        "    intersection = len(reference_set & output_set)\n",
        "    union = len(reference_set | output_set)\n",
        "    jaccard_index = intersection / union if union > 0 else 0\n",
        "    return jaccard_index"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "VkIi5KCGvu42"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "def calculate_cosine_similarity(text1, text2):\n",
        "    vectorizer = CountVectorizer().fit_transform([text1, text2])\n",
        "    vectors = vectorizer.toarray()\n",
        "    cos_sim = cosine_similarity(vectors)[0][1]\n",
        "\n",
        "    return cos_sim"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "80okNADtm9BJ"
      },
      "outputs": [],
      "source": [
        "def print_file_contents(file_path):\n",
        "    try:\n",
        "        with open(file_path, 'r', encoding='utf-8') as file:\n",
        "            contents = file.read()\n",
        "            print(contents)\n",
        "    except FileNotFoundError:\n",
        "        print(f\"Error: The file '{file_path}' was not found.\")\n",
        "    except Exception as e:\n",
        "        print(f\"Error: An unexpected error occurred - {e}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "qa6tfr3ov6lh"
      },
      "outputs": [],
      "source": [
        "def read_file(file_path):\n",
        "    try:\n",
        "        with open(file_path, 'r', encoding='utf-8') as file:\n",
        "            content = file.read()\n",
        "        return content\n",
        "    except FileNotFoundError:\n",
        "        print(f\"Error: The file '{file_path}' was not found.\")\n",
        "        return None\n",
        "    except Exception as e:\n",
        "        print(f\"Error: An unexpected error occurred - {e}\")\n",
        "        return None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UfXhf7e7r9U7"
      },
      "outputs": [],
      "source": [
        "starting_font = 12\n",
        "increment_font = 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "yhq59NQbmXWA"
      },
      "outputs": [],
      "source": [
        "def evaluate_ocr_models(num_docs):\n",
        "    ocr_models = {\n",
        "        \"gemini_pro\": extract_text_gemini,\n",
        "        \"gemini_flash\": extract_text_gemini,\n",
        "        \"opus\": extract_text_opus,\n",
        "        \"sonnet\": extract_text_sonnet,\n",
        "        \"haiku\": extract_text_haiku,\n",
        "        \"gpt4_turbo\": extract_text_gpt,\n",
        "        \"gpt4o\": extract_text_gpt,\n",
        "        \"vision\": extract_text_vision,\n",
        "        # \"marker\": None\n",
        "        }\n",
        "\n",
        "    evaluation_metrics = {\n",
        "        \"Fuzzy Score\": calculate_fuzzy_score,\n",
        "        \"CER\": calculate_cer,\n",
        "        \"WER\": calculate_wer,\n",
        "        \"BLEU\": calculate_bleu,\n",
        "        \"Jaccard Index\": calculate_jaccard_index,\n",
        "        \"Cosine Similarity\": calculate_cosine_similarity\n",
        "        }\n",
        "\n",
        "    results = {metric: {model: 0 for model in ocr_models} for metric in evaluation_metrics.keys()}\n",
        "\n",
        "    for model in ocr_models:\n",
        "        for metric in evaluation_metrics:\n",
        "            model_metrics = []\n",
        "            for docs in range(num_docs):\n",
        "                if model == \"marker\" and os.path.exists(f\"/content/{model}_output_{docs}.md\"):\n",
        "                  score = evaluation_metrics[metric](read_file(f\"/content/reference_{docs}.txt\"), read_file(f\"/content/{model}_output_{docs}.md\"))\n",
        "                elif os.path.exists(f\"/content/{model}_font_output_{starting_font + docs * increment_font}.txt\"):\n",
        "                  score = evaluation_metrics[metric](read_file(f\"/content/font-reference.txt\"), read_file(f\"/content/{model}_font_output_{starting_font + docs * increment_font}.txt\"))\n",
        "                else:\n",
        "                  continue\n",
        "                model_metrics.append(score)\n",
        "\n",
        "            for docs in range(1, 7):\n",
        "                if model == \"marker\" and os.path.exists(f\"/content/{model}_output_{docs}.md\"):\n",
        "                  score = evaluation_metrics[metric](read_file(f\"/content/reference_{docs}.txt\"), read_file(f\"/content/{model}_output_{docs}.md\"))\n",
        "                elif os.path.exists(f\"/content/{model}_output_{docs}.txt\"):\n",
        "                  score = evaluation_metrics[metric](read_file(f\"/content/reference_{docs}.txt\"), read_file(f\"/content/{model}_output_{docs}.txt\"))\n",
        "                else:\n",
        "                  continue\n",
        "                model_metrics.append(score)\n",
        "\n",
        "            results[metric][model] = mean(model_metrics)\n",
        "    scores_df = pd.DataFrame(results)\n",
        "    scores_df.index.name = 'Models'\n",
        "    return scores_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "LW806l1dsw5d"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "def evaluate_ocr_models_for_different_languages():\n",
        "    ocr_models = {\n",
        "        # \"gemini_flash\": extract_text_gemini,\n",
        "        # \"opus\": extract_text_opus,\n",
        "        # \"sonnet\": extract_text_sonnet,\n",
        "        # \"haiku\": extract_text_haiku,\n",
        "        # \"gpt4_turbo\": extract_text_gpt,\n",
        "        # \"gpt4o\": extract_text_gpt,\n",
        "        \"vision\": extract_text_vision,\n",
        "        # \"tesseract\": extract_text_tesseract,\n",
        "    }\n",
        "\n",
        "    evaluation_metrics = {\n",
        "        \"Fuzzy Score\": calculate_fuzzy_score,\n",
        "        \"CER\": calculate_cer,\n",
        "        \"WER\": calculate_wer,\n",
        "        # \"BLEU\": calculate_bleu,\n",
        "        \"Jaccard Index\": calculate_jaccard_index,\n",
        "        \"Cosine Similarity\": calculate_cosine_similarity\n",
        "    }\n",
        "\n",
        "    results = {model: {metric: [] for metric in evaluation_metrics} for model in ocr_models}\n",
        "\n",
        "    for model in ocr_models:\n",
        "        for language in languages:\n",
        "            model_output_path = f\"/content/{model}_{language}_output.txt\"\n",
        "            reference_path = f\"/content/{language}_reference.txt\"\n",
        "\n",
        "            if os.path.exists(model_output_path):\n",
        "                for metric in evaluation_metrics:\n",
        "                    score = evaluation_metrics[metric](read_file(reference_path), read_file(model_output_path))\n",
        "                    results[model][metric].append(score)\n",
        "            else:\n",
        "                print(reference_path)\n",
        "                for metric in evaluation_metrics:\n",
        "                    print(model_output_path)\n",
        "                    results[model][metric].append(None)\n",
        "\n",
        "    models_dfs = {}\n",
        "    for model, metrics_scores in results.items():\n",
        "        df = pd.DataFrame(metrics_scores)\n",
        "        df.index = [language for language in languages]\n",
        "        df.index.name = 'Languages'\n",
        "        models_dfs[model] = df\n",
        "\n",
        "    return models_dfs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XXzL1iPNCemd",
        "outputId": "b244ef17-5988-4847-90f9-078ce9ed0285"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: opus\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"Languages\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"arabic\",\n          \"bengali\",\n          \"greek\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 28,\n        \"max\": 99,\n        \"num_unique_values\": 15,\n        \"samples\": [\n          90,\n          28,\n          76\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.29423382595625325,\n        \"min\": 0.007773985589685248,\n        \"max\": 0.8049417436721575,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.3767572633552015,\n          0.6662768031189084,\n          0.04246440844378989\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.799784066203956,\n        \"min\": 0.026106696935300794,\n        \"max\": 12.82857142857143,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          2.3532934131736525,\n          4.792613636363637,\n          0.2553542009884679\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1722359278462858,\n        \"min\": 0.4127906976744186,\n        \"max\": 0.9814814814814815,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.8627450980392157,\n          0.8153846153846154,\n          0.9710144927536232\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.30157125562544634,\n        \"min\": 0.08928163578007509,\n        \"max\": 0.9994166695838832,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.875069029090396,\n          0.5306427852649345,\n          0.9927171409278328\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-489cb91b-1dc1-4243-aa7a-56a8781f0e70\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
              "      <th>Cosine Similarity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Languages</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>arabic</th>\n",
              "      <td>76</td>\n",
              "      <td>0.376757</td>\n",
              "      <td>2.353293</td>\n",
              "      <td>0.862745</td>\n",
              "      <td>0.875069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bengali</th>\n",
              "      <td>74</td>\n",
              "      <td>0.666277</td>\n",
              "      <td>4.792614</td>\n",
              "      <td>0.815385</td>\n",
              "      <td>0.530643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chinese</th>\n",
              "      <td>62</td>\n",
              "      <td>0.353923</td>\n",
              "      <td>9.975610</td>\n",
              "      <td>0.573086</td>\n",
              "      <td>0.398227</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cyrillic</th>\n",
              "      <td>95</td>\n",
              "      <td>0.040299</td>\n",
              "      <td>0.242165</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>0.987005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dutch</th>\n",
              "      <td>94</td>\n",
              "      <td>0.048046</td>\n",
              "      <td>0.280783</td>\n",
              "      <td>0.942857</td>\n",
              "      <td>0.992913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>english</th>\n",
              "      <td>97</td>\n",
              "      <td>0.045094</td>\n",
              "      <td>0.216625</td>\n",
              "      <td>0.954545</td>\n",
              "      <td>0.997309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>french</th>\n",
              "      <td>99</td>\n",
              "      <td>0.007774</td>\n",
              "      <td>0.026107</td>\n",
              "      <td>0.979592</td>\n",
              "      <td>0.999417</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>german</th>\n",
              "      <td>96</td>\n",
              "      <td>0.049643</td>\n",
              "      <td>0.311236</td>\n",
              "      <td>0.981481</td>\n",
              "      <td>0.994753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>greek</th>\n",
              "      <td>96</td>\n",
              "      <td>0.042464</td>\n",
              "      <td>0.255354</td>\n",
              "      <td>0.971014</td>\n",
              "      <td>0.992717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hebrew</th>\n",
              "      <td>44</td>\n",
              "      <td>0.804942</td>\n",
              "      <td>4.868712</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.292718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hindi</th>\n",
              "      <td>90</td>\n",
              "      <td>0.250994</td>\n",
              "      <td>1.209375</td>\n",
              "      <td>0.876923</td>\n",
              "      <td>0.975469</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>japanese</th>\n",
              "      <td>92</td>\n",
              "      <td>0.103740</td>\n",
              "      <td>7.592593</td>\n",
              "      <td>0.910798</td>\n",
              "      <td>0.915534</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>korean</th>\n",
              "      <td>28</td>\n",
              "      <td>0.804641</td>\n",
              "      <td>3.336268</td>\n",
              "      <td>0.412791</td>\n",
              "      <td>0.089282</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latin</th>\n",
              "      <td>97</td>\n",
              "      <td>0.027611</td>\n",
              "      <td>0.177043</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.985059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spanish</th>\n",
              "      <td>99</td>\n",
              "      <td>0.019810</td>\n",
              "      <td>0.092405</td>\n",
              "      <td>0.937500</td>\n",
              "      <td>0.996633</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thai</th>\n",
              "      <td>67</td>\n",
              "      <td>0.463868</td>\n",
              "      <td>12.828571</td>\n",
              "      <td>0.678571</td>\n",
              "      <td>0.659937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>urdu</th>\n",
              "      <td>49</td>\n",
              "      <td>0.714204</td>\n",
              "      <td>3.275132</td>\n",
              "      <td>0.883721</td>\n",
              "      <td>0.391585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vietnamese</th>\n",
              "      <td>84</td>\n",
              "      <td>0.132894</td>\n",
              "      <td>0.561207</td>\n",
              "      <td>0.960396</td>\n",
              "      <td>0.949777</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  <div class=\"colab-df-container\">\n",
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              "    .colab-df-convert {\n",
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            "text/plain": [
              "            Fuzzy Score       CER        WER  Jaccard Index  Cosine Similarity\n",
              "Languages                                                                     \n",
              "arabic               76  0.376757   2.353293       0.862745           0.875069\n",
              "bengali              74  0.666277   4.792614       0.815385           0.530643\n",
              "chinese              62  0.353923   9.975610       0.573086           0.398227\n",
              "cyrillic             95  0.040299   0.242165       0.963636           0.987005\n",
              "dutch                94  0.048046   0.280783       0.942857           0.992913\n",
              "english              97  0.045094   0.216625       0.954545           0.997309\n",
              "french               99  0.007774   0.026107       0.979592           0.999417\n",
              "german               96  0.049643   0.311236       0.981481           0.994753\n",
              "greek                96  0.042464   0.255354       0.971014           0.992717\n",
              "hebrew               44  0.804942   4.868712       0.555556           0.292718\n",
              "hindi                90  0.250994   1.209375       0.876923           0.975469\n",
              "japanese             92  0.103740   7.592593       0.910798           0.915534\n",
              "korean               28  0.804641   3.336268       0.412791           0.089282\n",
              "latin                97  0.027611   0.177043       0.962963           0.985059\n",
              "spanish              99  0.019810   0.092405       0.937500           0.996633\n",
              "thai                 67  0.463868  12.828571       0.678571           0.659937\n",
              "urdu                 49  0.714204   3.275132       0.883721           0.391585\n",
              "vietnamese           84  0.132894   0.561207       0.960396           0.949777"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Model: sonnet\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"Languages\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"arabic\",\n          \"bengali\",\n          \"greek\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 23,\n        \"max\": 99,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          65,\n          67,\n          98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3227746192270715,\n        \"min\": 0.022942737959802808,\n        \"max\": 0.8464106844741235,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.49609497032177446,\n          0.7703703703703704,\n          0.10898379970544919\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.061524934035456,\n        \"min\": 0.11464245175936436,\n        \"max\": 24.121951219512194,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          3.12375249500998,\n          5.488636363636363,\n          0.6869851729818781\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.21471218402735157,\n        \"min\": 0.18821603927986907,\n        \"max\": 0.9636363636363636,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.6805555555555556,\n          0.5977011494252874,\n          0.7528089887640449\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.340623867114622,\n        \"min\": 0.03106848830006,\n        \"max\": 0.9988109929760138,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.7658988355718965,\n          0.3788357860891555,\n          0.9797664355295872\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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            "text/html": [
              "\n",
              "  <div id=\"df-835d36e5-ce14-46af-9c77-c6013facfc62\" class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
              "      <th>Cosine Similarity</th>\n",
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              "      <th>Languages</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>arabic</th>\n",
              "      <td>65</td>\n",
              "      <td>0.496095</td>\n",
              "      <td>3.123752</td>\n",
              "      <td>0.680556</td>\n",
              "      <td>0.765899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bengali</th>\n",
              "      <td>67</td>\n",
              "      <td>0.770370</td>\n",
              "      <td>5.488636</td>\n",
              "      <td>0.597701</td>\n",
              "      <td>0.378836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chinese</th>\n",
              "      <td>23</td>\n",
              "      <td>0.846411</td>\n",
              "      <td>24.121951</td>\n",
              "      <td>0.188216</td>\n",
              "      <td>0.031068</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cyrillic</th>\n",
              "      <td>90</td>\n",
              "      <td>0.117815</td>\n",
              "      <td>0.710826</td>\n",
              "      <td>0.698630</td>\n",
              "      <td>0.963953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dutch</th>\n",
              "      <td>91</td>\n",
              "      <td>0.110134</td>\n",
              "      <td>0.715765</td>\n",
              "      <td>0.957746</td>\n",
              "      <td>0.991618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>english</th>\n",
              "      <td>98</td>\n",
              "      <td>0.037843</td>\n",
              "      <td>0.162469</td>\n",
              "      <td>0.954545</td>\n",
              "      <td>0.996665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>french</th>\n",
              "      <td>99</td>\n",
              "      <td>0.022943</td>\n",
              "      <td>0.114642</td>\n",
              "      <td>0.941176</td>\n",
              "      <td>0.998811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>german</th>\n",
              "      <td>94</td>\n",
              "      <td>0.104686</td>\n",
              "      <td>0.668539</td>\n",
              "      <td>0.963636</td>\n",
              "      <td>0.989543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>greek</th>\n",
              "      <td>92</td>\n",
              "      <td>0.108984</td>\n",
              "      <td>0.686985</td>\n",
              "      <td>0.752809</td>\n",
              "      <td>0.979766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hebrew</th>\n",
              "      <td>51</td>\n",
              "      <td>0.826436</td>\n",
              "      <td>4.969325</td>\n",
              "      <td>0.556962</td>\n",
              "      <td>0.319149</td>\n",
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              "      <th>hindi</th>\n",
              "      <td>77</td>\n",
              "      <td>0.579168</td>\n",
              "      <td>2.823958</td>\n",
              "      <td>0.694118</td>\n",
              "      <td>0.806080</td>\n",
              "    </tr>\n",
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              "      <th>japanese</th>\n",
              "      <td>67</td>\n",
              "      <td>0.302760</td>\n",
              "      <td>24.111111</td>\n",
              "      <td>0.676647</td>\n",
              "      <td>0.657504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>korean</th>\n",
              "      <td>28</td>\n",
              "      <td>0.801266</td>\n",
              "      <td>3.288732</td>\n",
              "      <td>0.397183</td>\n",
              "      <td>0.033994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latin</th>\n",
              "      <td>95</td>\n",
              "      <td>0.050274</td>\n",
              "      <td>0.317121</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.978361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spanish</th>\n",
              "      <td>98</td>\n",
              "      <td>0.027187</td>\n",
              "      <td>0.143038</td>\n",
              "      <td>0.877551</td>\n",
              "      <td>0.997151</td>\n",
              "    </tr>\n",
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              "      <th>thai</th>\n",
              "      <td>55</td>\n",
              "      <td>0.660560</td>\n",
              "      <td>18.328571</td>\n",
              "      <td>0.747475</td>\n",
              "      <td>0.388898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>urdu</th>\n",
              "      <td>52</td>\n",
              "      <td>0.695227</td>\n",
              "      <td>3.174603</td>\n",
              "      <td>0.569231</td>\n",
              "      <td>0.550558</td>\n",
              "    </tr>\n",
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              "      <th>vietnamese</th>\n",
              "      <td>79</td>\n",
              "      <td>0.243123</td>\n",
              "      <td>1.062931</td>\n",
              "      <td>0.872727</td>\n",
              "      <td>0.893903</td>\n",
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            ],
            "text/plain": [
              "            Fuzzy Score       CER        WER  Jaccard Index  Cosine Similarity\n",
              "Languages                                                                     \n",
              "arabic               65  0.496095   3.123752       0.680556           0.765899\n",
              "bengali              67  0.770370   5.488636       0.597701           0.378836\n",
              "chinese              23  0.846411  24.121951       0.188216           0.031068\n",
              "cyrillic             90  0.117815   0.710826       0.698630           0.963953\n",
              "dutch                91  0.110134   0.715765       0.957746           0.991618\n",
              "english              98  0.037843   0.162469       0.954545           0.996665\n",
              "french               99  0.022943   0.114642       0.941176           0.998811\n",
              "german               94  0.104686   0.668539       0.963636           0.989543\n",
              "greek                92  0.108984   0.686985       0.752809           0.979766\n",
              "hebrew               51  0.826436   4.969325       0.556962           0.319149\n",
              "hindi                77  0.579168   2.823958       0.694118           0.806080\n",
              "japanese             67  0.302760  24.111111       0.676647           0.657504\n",
              "korean               28  0.801266   3.288732       0.397183           0.033994\n",
              "latin                95  0.050274   0.317121       0.962963           0.978361\n",
              "spanish              98  0.027187   0.143038       0.877551           0.997151\n",
              "thai                 55  0.660560  18.328571       0.747475           0.388898\n",
              "urdu                 52  0.695227   3.174603       0.569231           0.550558\n",
              "vietnamese           79  0.243123   1.062931       0.872727           0.893903"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Model: haiku\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"Languages\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"arabic\",\n          \"bengali\",\n          \"greek\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34,\n        \"min\": 8,\n        \"max\": 99,\n        \"num_unique_values\": 16,\n        \"samples\": [\n          36,\n          45,\n          99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3595556787059576,\n        \"min\": 0.023113528212100613,\n        \"max\": 0.9941569282136895,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.7647610121836926,\n          0.8066276803118908,\n          0.7292587137947962\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.710004299638811,\n        \"min\": 0.1070528967254408,\n        \"max\": 63.370370370370374,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          4.874251497005988,\n          5.786931818181818,\n          4.85831960461285\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3191530126934353,\n        \"min\": 0.013888888888888888,\n        \"max\": 0.9571428571428572,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.4659090909090909,\n          0.5730337078651685,\n          0.3582089552238806\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.43168443386351163,\n        \"min\": 0.0,\n        \"max\": 0.9983834336252877,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          0.30342133556900935,\n          0.2608202926447102,\n          0.9983834336252877\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
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            "text/html": [
              "\n",
              "  <div id=\"df-f92e5670-1d34-43dc-8af7-2fc1dd4b691b\" class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
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              "      <th>Languages</th>\n",
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              "    <tr>\n",
              "      <th>arabic</th>\n",
              "      <td>36</td>\n",
              "      <td>0.764761</td>\n",
              "      <td>4.874251</td>\n",
              "      <td>0.465909</td>\n",
              "      <td>0.303421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bengali</th>\n",
              "      <td>45</td>\n",
              "      <td>0.806628</td>\n",
              "      <td>5.786932</td>\n",
              "      <td>0.573034</td>\n",
              "      <td>0.260820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chinese</th>\n",
              "      <td>14</td>\n",
              "      <td>0.994157</td>\n",
              "      <td>28.804878</td>\n",
              "      <td>0.193059</td>\n",
              "      <td>0.044445</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cyrillic</th>\n",
              "      <td>62</td>\n",
              "      <td>0.489099</td>\n",
              "      <td>3.149573</td>\n",
              "      <td>0.566265</td>\n",
              "      <td>0.800964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dutch</th>\n",
              "      <td>91</td>\n",
              "      <td>0.138048</td>\n",
              "      <td>0.892980</td>\n",
              "      <td>0.957143</td>\n",
              "      <td>0.991231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>english</th>\n",
              "      <td>99</td>\n",
              "      <td>0.023114</td>\n",
              "      <td>0.107053</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.998383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>french</th>\n",
              "      <td>82</td>\n",
              "      <td>0.286500</td>\n",
              "      <td>1.700341</td>\n",
              "      <td>0.807018</td>\n",
              "      <td>0.976977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>german</th>\n",
              "      <td>93</td>\n",
              "      <td>0.104860</td>\n",
              "      <td>0.670787</td>\n",
              "      <td>0.946429</td>\n",
              "      <td>0.990169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>greek</th>\n",
              "      <td>24</td>\n",
              "      <td>0.729259</td>\n",
              "      <td>4.858320</td>\n",
              "      <td>0.358209</td>\n",
              "      <td>0.248820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hebrew</th>\n",
              "      <td>36</td>\n",
              "      <td>0.786260</td>\n",
              "      <td>4.754601</td>\n",
              "      <td>0.537500</td>\n",
              "      <td>0.159440</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hindi</th>\n",
              "      <td>8</td>\n",
              "      <td>0.954821</td>\n",
              "      <td>4.756250</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>japanese</th>\n",
              "      <td>30</td>\n",
              "      <td>0.789403</td>\n",
              "      <td>63.370370</td>\n",
              "      <td>0.349680</td>\n",
              "      <td>0.123645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>korean</th>\n",
              "      <td>20</td>\n",
              "      <td>0.846414</td>\n",
              "      <td>3.521127</td>\n",
              "      <td>0.280443</td>\n",
              "      <td>0.027006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latin</th>\n",
              "      <td>97</td>\n",
              "      <td>0.039073</td>\n",
              "      <td>0.256809</td>\n",
              "      <td>0.894737</td>\n",
              "      <td>0.982940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spanish</th>\n",
              "      <td>98</td>\n",
              "      <td>0.044046</td>\n",
              "      <td>0.243038</td>\n",
              "      <td>0.860000</td>\n",
              "      <td>0.995875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thai</th>\n",
              "      <td>28</td>\n",
              "      <td>0.857252</td>\n",
              "      <td>23.671429</td>\n",
              "      <td>0.611111</td>\n",
              "      <td>0.153045</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>urdu</th>\n",
              "      <td>8</td>\n",
              "      <td>0.951121</td>\n",
              "      <td>4.375661</td>\n",
              "      <td>0.013889</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vietnamese</th>\n",
              "      <td>79</td>\n",
              "      <td>0.496900</td>\n",
              "      <td>2.204310</td>\n",
              "      <td>0.932039</td>\n",
              "      <td>0.915511</td>\n",
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            ],
            "text/plain": [
              "            Fuzzy Score       CER        WER  Jaccard Index  Cosine Similarity\n",
              "Languages                                                                     \n",
              "arabic               36  0.764761   4.874251       0.465909           0.303421\n",
              "bengali              45  0.806628   5.786932       0.573034           0.260820\n",
              "chinese              14  0.994157  28.804878       0.193059           0.044445\n",
              "cyrillic             62  0.489099   3.149573       0.566265           0.800964\n",
              "dutch                91  0.138048   0.892980       0.957143           0.991231\n",
              "english              99  0.023114   0.107053       0.933333           0.998383\n",
              "french               82  0.286500   1.700341       0.807018           0.976977\n",
              "german               93  0.104860   0.670787       0.946429           0.990169\n",
              "greek                24  0.729259   4.858320       0.358209           0.248820\n",
              "hebrew               36  0.786260   4.754601       0.537500           0.159440\n",
              "hindi                 8  0.954821   4.756250       0.032258           0.000000\n",
              "japanese             30  0.789403  63.370370       0.349680           0.123645\n",
              "korean               20  0.846414   3.521127       0.280443           0.027006\n",
              "latin                97  0.039073   0.256809       0.894737           0.982940\n",
              "spanish              98  0.044046   0.243038       0.860000           0.995875\n",
              "thai                 28  0.857252  23.671429       0.611111           0.153045\n",
              "urdu                  8  0.951121   4.375661       0.013889           0.000000\n",
              "vietnamese           79  0.496900   2.204310       0.932039           0.915511"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Model: gpt4_turbo\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"Languages\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"arabic\",\n          \"bengali\",\n          \"greek\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 21,\n        \"max\": 100,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          55,\n          78,\n          99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3877421701249285,\n        \"min\": 0.0027869709109911165,\n        \"max\": 1.2734177215189872,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.6807247735082786,\n          0.7037037037037037,\n          0.08051055473735887\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.591607212508988,\n        \"min\": 0.008988764044943821,\n        \"max\": 37.44444444444444,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          4.323353293413174,\n          5.065340909090909,\n          0.5090609555189456\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2609830573030153,\n        \"min\": 0.013071895424836602,\n        \"max\": 1.0,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          0.8333333333333334,\n          0.6265060240963856,\n          0.9333333333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.33479822045097335,\n        \"min\": 0.0,\n        \"max\": 0.9999206978588416,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.7219781551814013,\n          0.5410799913297344,\n          0.9886639701758357\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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            "text/html": [
              "\n",
              "  <div id=\"df-962db163-6111-4d97-9dd2-4a46c84a15ce\" class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
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              "      <th>Languages</th>\n",
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              "    <tr>\n",
              "      <th>arabic</th>\n",
              "      <td>55</td>\n",
              "      <td>0.680725</td>\n",
              "      <td>4.323353</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.721978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bengali</th>\n",
              "      <td>78</td>\n",
              "      <td>0.703704</td>\n",
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              "      <td>0.541080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chinese</th>\n",
              "      <td>23</td>\n",
              "      <td>0.824708</td>\n",
              "      <td>23.756098</td>\n",
              "      <td>0.205575</td>\n",
              "      <td>0.029604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cyrillic</th>\n",
              "      <td>90</td>\n",
              "      <td>0.095353</td>\n",
              "      <td>0.584046</td>\n",
              "      <td>0.697368</td>\n",
              "      <td>0.963841</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dutch</th>\n",
              "      <td>96</td>\n",
              "      <td>0.093724</td>\n",
              "      <td>0.604143</td>\n",
              "      <td>0.944444</td>\n",
              "      <td>0.990596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>english</th>\n",
              "      <td>99</td>\n",
              "      <td>0.021074</td>\n",
              "      <td>0.090680</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.998749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>french</th>\n",
              "      <td>95</td>\n",
              "      <td>0.083239</td>\n",
              "      <td>0.467650</td>\n",
              "      <td>0.842105</td>\n",
              "      <td>0.989760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>german</th>\n",
              "      <td>100</td>\n",
              "      <td>0.002787</td>\n",
              "      <td>0.008989</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>greek</th>\n",
              "      <td>93</td>\n",
              "      <td>0.080511</td>\n",
              "      <td>0.509061</td>\n",
              "      <td>0.930556</td>\n",
              "      <td>0.988664</td>\n",
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              "    <tr>\n",
              "      <th>hebrew</th>\n",
              "      <td>54</td>\n",
              "      <td>0.857372</td>\n",
              "      <td>5.174233</td>\n",
              "      <td>0.681159</td>\n",
              "      <td>0.379806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hindi</th>\n",
              "      <td>72</td>\n",
              "      <td>0.628320</td>\n",
              "      <td>3.112500</td>\n",
              "      <td>0.907692</td>\n",
              "      <td>0.697304</td>\n",
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              "    <tr>\n",
              "      <th>japanese</th>\n",
              "      <td>64</td>\n",
              "      <td>0.461264</td>\n",
              "      <td>37.444444</td>\n",
              "      <td>0.644737</td>\n",
              "      <td>0.550642</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>korean</th>\n",
              "      <td>21</td>\n",
              "      <td>1.273418</td>\n",
              "      <td>5.221831</td>\n",
              "      <td>0.013072</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latin</th>\n",
              "      <td>92</td>\n",
              "      <td>0.191456</td>\n",
              "      <td>1.387160</td>\n",
              "      <td>0.896552</td>\n",
              "      <td>0.927789</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spanish</th>\n",
              "      <td>99</td>\n",
              "      <td>0.028030</td>\n",
              "      <td>0.139241</td>\n",
              "      <td>0.843137</td>\n",
              "      <td>0.997357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thai</th>\n",
              "      <td>56</td>\n",
              "      <td>0.852417</td>\n",
              "      <td>23.771429</td>\n",
              "      <td>0.728155</td>\n",
              "      <td>0.360498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>urdu</th>\n",
              "      <td>52</td>\n",
              "      <td>0.714204</td>\n",
              "      <td>3.267196</td>\n",
              "      <td>0.622951</td>\n",
              "      <td>0.720733</td>\n",
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              "      <th>vietnamese</th>\n",
              "      <td>85</td>\n",
              "      <td>0.246610</td>\n",
              "      <td>1.063793</td>\n",
              "      <td>0.933333</td>\n",
              "      <td>0.947641</td>\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e8cd9c93-f44b-4952-b357-ed74686c4e11 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            Fuzzy Score       CER        WER  Jaccard Index  Cosine Similarity\n",
              "Languages                                                                     \n",
              "arabic               55  0.680725   4.323353       0.833333           0.721978\n",
              "bengali              78  0.703704   5.065341       0.626506           0.541080\n",
              "chinese              23  0.824708  23.756098       0.205575           0.029604\n",
              "cyrillic             90  0.095353   0.584046       0.697368           0.963841\n",
              "dutch                96  0.093724   0.604143       0.944444           0.990596\n",
              "english              99  0.021074   0.090680       0.933333           0.998749\n",
              "french               95  0.083239   0.467650       0.842105           0.989760\n",
              "german              100  0.002787   0.008989       1.000000           0.999921\n",
              "greek                93  0.080511   0.509061       0.930556           0.988664\n",
              "hebrew               54  0.857372   5.174233       0.681159           0.379806\n",
              "hindi                72  0.628320   3.112500       0.907692           0.697304\n",
              "japanese             64  0.461264  37.444444       0.644737           0.550642\n",
              "korean               21  1.273418   5.221831       0.013072           0.000000\n",
              "latin                92  0.191456   1.387160       0.896552           0.927789\n",
              "spanish              99  0.028030   0.139241       0.843137           0.997357\n",
              "thai                 56  0.852417  23.771429       0.728155           0.360498\n",
              "urdu                 52  0.714204   3.267196       0.622951           0.720733\n",
              "vietnamese           85  0.246610   1.063793       0.933333           0.947641"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Model: gpt4o\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"Languages\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"arabic\",\n          \"bengali\",\n          \"greek\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 61,\n        \"max\": 100,\n        \"num_unique_values\": 13,\n        \"samples\": [\n          81,\n          93,\n          96\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2225394916433416,\n        \"min\": 0.004305461137548153,\n        \"max\": 0.7400805060379528,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.06310527960012496,\n          0.4619883040935672,\n          0.04639175257731959\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0907817978978644,\n        \"min\": 0.003778337531486146,\n        \"max\": 5.957142857142857,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.34530938123752497,\n          3.3238636363636362,\n          0.2355848434925865\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14751181897243867,\n        \"min\": 0.5068119891008175,\n        \"max\": 0.9782608695652174,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.6712328767123288,\n          0.9016393442622951,\n          0.7282608695652174\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11483698205261142,\n        \"min\": 0.5965830453920575,\n        \"max\": 0.9999999999999993,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.9809529877631776,\n          0.8236557439357621,\n          0.9931121552504696\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-39d51cba-5c33-465e-8690-b5f25630914a\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
              "      <th>Cosine Similarity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Languages</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>arabic</th>\n",
              "      <td>96</td>\n",
              "      <td>0.063105</td>\n",
              "      <td>0.345309</td>\n",
              "      <td>0.671233</td>\n",
              "      <td>0.980953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bengali</th>\n",
              "      <td>86</td>\n",
              "      <td>0.461988</td>\n",
              "      <td>3.323864</td>\n",
              "      <td>0.901639</td>\n",
              "      <td>0.823656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chinese</th>\n",
              "      <td>73</td>\n",
              "      <td>0.194491</td>\n",
              "      <td>5.365854</td>\n",
              "      <td>0.759690</td>\n",
              "      <td>0.596583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cyrillic</th>\n",
              "      <td>97</td>\n",
              "      <td>0.041621</td>\n",
              "      <td>0.216524</td>\n",
              "      <td>0.726027</td>\n",
              "      <td>0.988776</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dutch</th>\n",
              "      <td>99</td>\n",
              "      <td>0.030452</td>\n",
              "      <td>0.177215</td>\n",
              "      <td>0.906667</td>\n",
              "      <td>0.998459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>english</th>\n",
              "      <td>100</td>\n",
              "      <td>0.004305</td>\n",
              "      <td>0.003778</td>\n",
              "      <td>0.976744</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>french</th>\n",
              "      <td>98</td>\n",
              "      <td>0.047023</td>\n",
              "      <td>0.264472</td>\n",
              "      <td>0.923077</td>\n",
              "      <td>0.999005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>german</th>\n",
              "      <td>99</td>\n",
              "      <td>0.028566</td>\n",
              "      <td>0.141573</td>\n",
              "      <td>0.946429</td>\n",
              "      <td>0.997423</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>greek</th>\n",
              "      <td>98</td>\n",
              "      <td>0.046392</td>\n",
              "      <td>0.235585</td>\n",
              "      <td>0.728261</td>\n",
              "      <td>0.993112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hebrew</th>\n",
              "      <td>68</td>\n",
              "      <td>0.453194</td>\n",
              "      <td>2.714110</td>\n",
              "      <td>0.522727</td>\n",
              "      <td>0.775091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hindi</th>\n",
              "      <td>91</td>\n",
              "      <td>0.348672</td>\n",
              "      <td>1.659375</td>\n",
              "      <td>0.797297</td>\n",
              "      <td>0.956740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>japanese</th>\n",
              "      <td>93</td>\n",
              "      <td>0.076135</td>\n",
              "      <td>5.296296</td>\n",
              "      <td>0.935714</td>\n",
              "      <td>0.920010</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>korean</th>\n",
              "      <td>61</td>\n",
              "      <td>0.525316</td>\n",
              "      <td>2.133803</td>\n",
              "      <td>0.506812</td>\n",
              "      <td>0.822502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latin</th>\n",
              "      <td>98</td>\n",
              "      <td>0.046106</td>\n",
              "      <td>0.280156</td>\n",
              "      <td>0.928571</td>\n",
              "      <td>0.985503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spanish</th>\n",
              "      <td>99</td>\n",
              "      <td>0.007587</td>\n",
              "      <td>0.022785</td>\n",
              "      <td>0.978261</td>\n",
              "      <td>0.997419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thai</th>\n",
              "      <td>81</td>\n",
              "      <td>0.217812</td>\n",
              "      <td>5.957143</td>\n",
              "      <td>0.777778</td>\n",
              "      <td>0.860192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>urdu</th>\n",
              "      <td>70</td>\n",
              "      <td>0.740081</td>\n",
              "      <td>3.375661</td>\n",
              "      <td>0.745098</td>\n",
              "      <td>0.777132</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vietnamese</th>\n",
              "      <td>98</td>\n",
              "      <td>0.029640</td>\n",
              "      <td>0.112931</td>\n",
              "      <td>0.951456</td>\n",
              "      <td>0.997747</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a4be0f2b-72ad-44df-ada1-d30430b2b66a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            Fuzzy Score       CER       WER  Jaccard Index  Cosine Similarity\n",
              "Languages                                                                    \n",
              "arabic               96  0.063105  0.345309       0.671233           0.980953\n",
              "bengali              86  0.461988  3.323864       0.901639           0.823656\n",
              "chinese              73  0.194491  5.365854       0.759690           0.596583\n",
              "cyrillic             97  0.041621  0.216524       0.726027           0.988776\n",
              "dutch                99  0.030452  0.177215       0.906667           0.998459\n",
              "english             100  0.004305  0.003778       0.976744           1.000000\n",
              "french               98  0.047023  0.264472       0.923077           0.999005\n",
              "german               99  0.028566  0.141573       0.946429           0.997423\n",
              "greek                98  0.046392  0.235585       0.728261           0.993112\n",
              "hebrew               68  0.453194  2.714110       0.522727           0.775091\n",
              "hindi                91  0.348672  1.659375       0.797297           0.956740\n",
              "japanese             93  0.076135  5.296296       0.935714           0.920010\n",
              "korean               61  0.525316  2.133803       0.506812           0.822502\n",
              "latin                98  0.046106  0.280156       0.928571           0.985503\n",
              "spanish              99  0.007587  0.022785       0.978261           0.997419\n",
              "thai                 81  0.217812  5.957143       0.777778           0.860192\n",
              "urdu                 70  0.740081  3.375661       0.745098           0.777132\n",
              "vietnamese           98  0.029640  0.112931       0.951456           0.997747"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Model: vision\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"Languages\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"arabic\",\n          \"bengali\",\n          \"greek\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 25,\n        \"max\": 100,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          25,\n          75,\n          100\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1993613749398623,\n        \"min\": 0.020288206295032234,\n        \"max\": 0.6714158504007124,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.030927835051546393,\n          0.0354775828460039,\n          0.03043691703485518\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.85374771439189,\n        \"min\": 0.075,\n        \"max\": 52.96296296296296,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.15568862275449102,\n          0.1534090909090909,\n          0.16803953871499178\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02895347975962444,\n        \"min\": 0.88,\n        \"max\": 0.9953917050691244,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          0.98,\n          0.9827586206896551,\n          0.9767441860465116\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3480704012555531,\n        \"min\": 0.0042342482556298085,\n        \"max\": 1.0000000000000036,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          1.0000000000000036,\n          1.0000000000000018,\n          0.9999999999999993\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-594eecec-abbc-4bee-8c78-721bd074fc97\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
              "      <th>Cosine Similarity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Languages</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>arabic</th>\n",
              "      <td>100</td>\n",
              "      <td>0.030928</td>\n",
              "      <td>0.155689</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bengali</th>\n",
              "      <td>100</td>\n",
              "      <td>0.035478</td>\n",
              "      <td>0.153409</td>\n",
              "      <td>0.982759</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chinese</th>\n",
              "      <td>25</td>\n",
              "      <td>0.627713</td>\n",
              "      <td>18.024390</td>\n",
              "      <td>0.908832</td>\n",
              "      <td>0.004234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cyrillic</th>\n",
              "      <td>100</td>\n",
              "      <td>0.037657</td>\n",
              "      <td>0.212251</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.999906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dutch</th>\n",
              "      <td>100</td>\n",
              "      <td>0.025207</td>\n",
              "      <td>0.140391</td>\n",
              "      <td>0.957746</td>\n",
              "      <td>0.999881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>english</th>\n",
              "      <td>100</td>\n",
              "      <td>0.024473</td>\n",
              "      <td>0.107053</td>\n",
              "      <td>0.976744</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>french</th>\n",
              "      <td>100</td>\n",
              "      <td>0.020288</td>\n",
              "      <td>0.094211</td>\n",
              "      <td>0.959184</td>\n",
              "      <td>0.999753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>german</th>\n",
              "      <td>100</td>\n",
              "      <td>0.041282</td>\n",
              "      <td>0.225843</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>greek</th>\n",
              "      <td>100</td>\n",
              "      <td>0.030437</td>\n",
              "      <td>0.168040</td>\n",
              "      <td>0.985294</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hebrew</th>\n",
              "      <td>98</td>\n",
              "      <td>0.046806</td>\n",
              "      <td>0.240491</td>\n",
              "      <td>0.979167</td>\n",
              "      <td>0.992660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hindi</th>\n",
              "      <td>99</td>\n",
              "      <td>0.038486</td>\n",
              "      <td>0.167708</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.998818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>japanese</th>\n",
              "      <td>29</td>\n",
              "      <td>0.671416</td>\n",
              "      <td>52.962963</td>\n",
              "      <td>0.949398</td>\n",
              "      <td>0.031592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>korean</th>\n",
              "      <td>75</td>\n",
              "      <td>0.173840</td>\n",
              "      <td>0.686620</td>\n",
              "      <td>0.995392</td>\n",
              "      <td>0.448421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latin</th>\n",
              "      <td>100</td>\n",
              "      <td>0.027611</td>\n",
              "      <td>0.169261</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spanish</th>\n",
              "      <td>99</td>\n",
              "      <td>0.060906</td>\n",
              "      <td>0.336709</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.997281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thai</th>\n",
              "      <td>59</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>5.142857</td>\n",
              "      <td>0.974684</td>\n",
              "      <td>0.373060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>urdu</th>\n",
              "      <td>96</td>\n",
              "      <td>0.038528</td>\n",
              "      <td>0.153439</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>0.954732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vietnamese</th>\n",
              "      <td>99</td>\n",
              "      <td>0.021310</td>\n",
              "      <td>0.075000</td>\n",
              "      <td>0.989899</td>\n",
              "      <td>0.998874</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8e68abaf-2826-4582-a124-d4128acaab37 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            Fuzzy Score       CER        WER  Jaccard Index  Cosine Similarity\n",
              "Languages                                                                     \n",
              "arabic              100  0.030928   0.155689       0.980000           1.000000\n",
              "bengali             100  0.035478   0.153409       0.982759           1.000000\n",
              "chinese              25  0.627713  18.024390       0.908832           0.004234\n",
              "cyrillic            100  0.037657   0.212251       0.962963           0.999906\n",
              "dutch               100  0.025207   0.140391       0.957746           0.999881\n",
              "english             100  0.024473   0.107053       0.976744           1.000000\n",
              "french              100  0.020288   0.094211       0.959184           0.999753\n",
              "german              100  0.041282   0.225843       0.981132           1.000000\n",
              "greek               100  0.030437   0.168040       0.985294           1.000000\n",
              "hebrew               98  0.046806   0.240491       0.979167           0.992660\n",
              "hindi                99  0.038486   0.167708       0.966667           0.998818\n",
              "japanese             29  0.671416  52.962963       0.949398           0.031592\n",
              "korean               75  0.173840   0.686620       0.995392           0.448421\n",
              "latin               100  0.027611   0.169261       0.981132           1.000000\n",
              "spanish              99  0.060906   0.336709       0.880000           0.997281\n",
              "thai                 59  0.200000   5.142857       0.974684           0.373060\n",
              "urdu                 96  0.038528   0.153439       0.975000           0.954732\n",
              "vietnamese           99  0.021310   0.075000       0.989899           0.998874"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Model: tesseract\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"Languages\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"arabic\",\n          \"bengali\",\n          \"greek\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34,\n        \"min\": 7,\n        \"max\": 94,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          24,\n          88,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3945193578603726,\n        \"min\": 0.045885475919605616,\n        \"max\": 1.0244879786286731,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.8706654170571696,\n          0.8631578947368421,\n          0.8576337751595483\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.218444781185273,\n        \"min\": 0.188422247446084,\n        \"max\": 84.37037037037037,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          5.500998003992016,\n          6.215909090909091,\n          5.660626029654036\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34968392223635286,\n        \"min\": 0.014336917562724014,\n        \"max\": 0.8936170212765957,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.09174311926605505,\n          0.027777777777777776,\n          0.13385826771653545\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4392089361824662,\n        \"min\": 0.0,\n        \"max\": 0.9768747457106202,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.02020536020703633,\n          0.913915025058188,\n          0.0006574697271499722\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-21bdf88f-b987-404a-ac4d-9c7a1fc1e6b1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
              "      <th>Cosine Similarity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Languages</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>arabic</th>\n",
              "      <td>18</td>\n",
              "      <td>0.870665</td>\n",
              "      <td>5.500998</td>\n",
              "      <td>0.091743</td>\n",
              "      <td>0.000657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bengali</th>\n",
              "      <td>18</td>\n",
              "      <td>0.863158</td>\n",
              "      <td>6.215909</td>\n",
              "      <td>0.027778</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chinese</th>\n",
              "      <td>10</td>\n",
              "      <td>0.991653</td>\n",
              "      <td>28.609756</td>\n",
              "      <td>0.025974</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cyrillic</th>\n",
              "      <td>15</td>\n",
              "      <td>0.881964</td>\n",
              "      <td>5.547009</td>\n",
              "      <td>0.051282</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dutch</th>\n",
              "      <td>86</td>\n",
              "      <td>0.089156</td>\n",
              "      <td>0.455696</td>\n",
              "      <td>0.853333</td>\n",
              "      <td>0.967344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>english</th>\n",
              "      <td>94</td>\n",
              "      <td>0.052799</td>\n",
              "      <td>0.225441</td>\n",
              "      <td>0.893617</td>\n",
              "      <td>0.976875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>french</th>\n",
              "      <td>93</td>\n",
              "      <td>0.045885</td>\n",
              "      <td>0.188422</td>\n",
              "      <td>0.807692</td>\n",
              "      <td>0.966458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>german</th>\n",
              "      <td>76</td>\n",
              "      <td>0.172792</td>\n",
              "      <td>1.053933</td>\n",
              "      <td>0.770492</td>\n",
              "      <td>0.753715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>greek</th>\n",
              "      <td>16</td>\n",
              "      <td>0.857634</td>\n",
              "      <td>5.660626</td>\n",
              "      <td>0.133858</td>\n",
              "      <td>0.004431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hebrew</th>\n",
              "      <td>21</td>\n",
              "      <td>0.857372</td>\n",
              "      <td>5.159509</td>\n",
              "      <td>0.178218</td>\n",
              "      <td>0.004796</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hindi</th>\n",
              "      <td>18</td>\n",
              "      <td>0.876177</td>\n",
              "      <td>4.308333</td>\n",
              "      <td>0.032258</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>japanese</th>\n",
              "      <td>15</td>\n",
              "      <td>1.024488</td>\n",
              "      <td>84.370370</td>\n",
              "      <td>0.025862</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>korean</th>\n",
              "      <td>24</td>\n",
              "      <td>0.886920</td>\n",
              "      <td>3.642606</td>\n",
              "      <td>0.014337</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latin</th>\n",
              "      <td>75</td>\n",
              "      <td>0.131545</td>\n",
              "      <td>0.817121</td>\n",
              "      <td>0.774194</td>\n",
              "      <td>0.888726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spanish</th>\n",
              "      <td>88</td>\n",
              "      <td>0.093151</td>\n",
              "      <td>0.470886</td>\n",
              "      <td>0.622951</td>\n",
              "      <td>0.913915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thai</th>\n",
              "      <td>7</td>\n",
              "      <td>0.944020</td>\n",
              "      <td>26.264286</td>\n",
              "      <td>0.163934</td>\n",
              "      <td>0.020205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>urdu</th>\n",
              "      <td>10</td>\n",
              "      <td>0.939620</td>\n",
              "      <td>4.293651</td>\n",
              "      <td>0.022472</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vietnamese</th>\n",
              "      <td>56</td>\n",
              "      <td>0.397714</td>\n",
              "      <td>1.718103</td>\n",
              "      <td>0.327586</td>\n",
              "      <td>0.119540</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21bdf88f-b987-404a-ac4d-9c7a1fc1e6b1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-21bdf88f-b987-404a-ac4d-9c7a1fc1e6b1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-21bdf88f-b987-404a-ac4d-9c7a1fc1e6b1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-bb86afb2-336c-44a3-a641-2d42914987a4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bb86afb2-336c-44a3-a641-2d42914987a4')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bb86afb2-336c-44a3-a641-2d42914987a4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "            Fuzzy Score       CER        WER  Jaccard Index  Cosine Similarity\n",
              "Languages                                                                     \n",
              "arabic               18  0.870665   5.500998       0.091743           0.000657\n",
              "bengali              18  0.863158   6.215909       0.027778           0.000000\n",
              "chinese              10  0.991653  28.609756       0.025974           0.000000\n",
              "cyrillic             15  0.881964   5.547009       0.051282           0.000000\n",
              "dutch                86  0.089156   0.455696       0.853333           0.967344\n",
              "english              94  0.052799   0.225441       0.893617           0.976875\n",
              "french               93  0.045885   0.188422       0.807692           0.966458\n",
              "german               76  0.172792   1.053933       0.770492           0.753715\n",
              "greek                16  0.857634   5.660626       0.133858           0.004431\n",
              "hebrew               21  0.857372   5.159509       0.178218           0.004796\n",
              "hindi                18  0.876177   4.308333       0.032258           0.000000\n",
              "japanese             15  1.024488  84.370370       0.025862           0.000000\n",
              "korean               24  0.886920   3.642606       0.014337           0.000000\n",
              "latin                75  0.131545   0.817121       0.774194           0.888726\n",
              "spanish              88  0.093151   0.470886       0.622951           0.913915\n",
              "thai                  7  0.944020  26.264286       0.163934           0.020205\n",
              "urdu                 10  0.939620   4.293651       0.022472           0.000000\n",
              "vietnamese           56  0.397714   1.718103       0.327586           0.119540"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "evaluation_results = evaluate_ocr_models_for_different_languages()\n",
        "\n",
        "for model, df in evaluation_results.items():\n",
        "    print(f\"Model: {model}\")\n",
        "    display(df)\n",
        "    print(\"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "4YwFT6BjEhwj"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_languages(models_dfs, evaluation_metric):\n",
        "    fig, axs = plt.subplots(4, 2, figsize=(25, 20))  # Create a 3x2 grid of subplots\n",
        "    fig.suptitle(f'{evaluation_metric} for Different OCR Models', fontsize=16)\n",
        "\n",
        "    models = list(models_dfs.keys())\n",
        "\n",
        "    for i, model in enumerate(models):\n",
        "        row = i // 2\n",
        "        col = i % 2\n",
        "        ax = axs[row, col]\n",
        "\n",
        "        df = models_dfs[model]\n",
        "\n",
        "        if evaluation_metric in df.columns:\n",
        "            ax.bar(df.index, df[evaluation_metric], color=\"#5675a8\")\n",
        "            ax.set_title(model)\n",
        "            ax.set_xlabel('Languages')\n",
        "            ax.set_ylabel(evaluation_metric)\n",
        "\n",
        "        else:\n",
        "            ax.text(0.5, 0.5, 'Metric Not Available', horizontalalignment='center', verticalalignment='center', transform=ax.transAxes)\n",
        "            ax.set_title(model)\n",
        "            ax.set_xlabel('Font Size')\n",
        "            ax.set_ylabel(evaluation_metric)\n",
        "\n",
        "    for j in range(len(models), 6):\n",
        "        row = j // 2\n",
        "        col = j % 2\n",
        "        fig.delaxes(axs[row, col])\n",
        "\n",
        "    plt.tight_layout(rect=[0, 0.03, 1, 0.95])  # Adjust layout to fit title\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 816
        },
        "id": "HfI2PsqsEmuE",
        "outputId": "0e643da5-f1b0-402e-882a-6f6a17ba4209"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 2500x2000 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_languages(evaluation_results, \"Fuzzy Score\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_languages(evaluation_results, \"Fuzzy Score\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 943
        },
        "collapsed": true,
        "id": "kFi32CB1Bvt8",
        "outputId": "7e4cbfef-0d86-4849-fb23-09a921742d28"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x2000 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "evaluation_results = evaluate_ocr_models_for_different_languages()\n",
        "\n",
        "for model, df in evaluation_results.items():\n",
        "    print(f\"Model: {model}\")\n",
        "    display(df)\n",
        "    print(\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "collapsed": true,
        "id": "UuWDxHtjBy06",
        "outputId": "0ced273e-cc3e-43d4-cbd1-2026d925fb5b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: vision\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "            Fuzzy Score       CER        WER  Jaccard Index  Cosine Similarity\n",
              "Languages                                                                     \n",
              "arabic              100  0.030928   0.155689       0.980000           1.000000\n",
              "bengali             100  0.035478   0.153409       0.982759           1.000000\n",
              "chinese              24  0.604341  17.268293       0.980057           0.006081\n",
              "cyrillic            100  0.037657   0.212251       0.962963           0.999906\n",
              "dutch               100  0.025207   0.140391       0.957746           0.999881\n",
              "english             100  0.024473   0.107053       0.976744           1.000000\n",
              "french              100  0.020288   0.094211       0.959184           0.999753\n",
              "german              100  0.041282   0.225843       0.981132           1.000000\n",
              "greek               100  0.030437   0.168040       0.985294           1.000000\n",
              "hebrew               98  0.046806   0.240491       0.979167           0.992660\n",
              "hindi                99  0.038486   0.167708       0.966667           0.998818\n",
              "japanese             29  0.674087  53.259259       0.946988           0.034352\n",
              "korean               75  0.173840   0.686620       0.995392           0.448421\n",
              "latin               100  0.027611   0.169261       0.981132           1.000000\n",
              "spanish              99  0.060906   0.336709       0.880000           0.997281\n",
              "thai                 59  0.200000   5.142857       0.974684           0.373060\n",
              "urdu                 96  0.038528   0.153439       0.975000           0.954732\n",
              "vietnamese           99  0.021310   0.075000       0.989899           0.998874"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d8f11083-08f8-46d0-9342-1f9817274aeb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
              "      <th>Cosine Similarity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Languages</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>arabic</th>\n",
              "      <td>100</td>\n",
              "      <td>0.030928</td>\n",
              "      <td>0.155689</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>bengali</th>\n",
              "      <td>100</td>\n",
              "      <td>0.035478</td>\n",
              "      <td>0.153409</td>\n",
              "      <td>0.982759</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>chinese</th>\n",
              "      <td>24</td>\n",
              "      <td>0.604341</td>\n",
              "      <td>17.268293</td>\n",
              "      <td>0.980057</td>\n",
              "      <td>0.006081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cyrillic</th>\n",
              "      <td>100</td>\n",
              "      <td>0.037657</td>\n",
              "      <td>0.212251</td>\n",
              "      <td>0.962963</td>\n",
              "      <td>0.999906</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dutch</th>\n",
              "      <td>100</td>\n",
              "      <td>0.025207</td>\n",
              "      <td>0.140391</td>\n",
              "      <td>0.957746</td>\n",
              "      <td>0.999881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>english</th>\n",
              "      <td>100</td>\n",
              "      <td>0.024473</td>\n",
              "      <td>0.107053</td>\n",
              "      <td>0.976744</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>french</th>\n",
              "      <td>100</td>\n",
              "      <td>0.020288</td>\n",
              "      <td>0.094211</td>\n",
              "      <td>0.959184</td>\n",
              "      <td>0.999753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>german</th>\n",
              "      <td>100</td>\n",
              "      <td>0.041282</td>\n",
              "      <td>0.225843</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>greek</th>\n",
              "      <td>100</td>\n",
              "      <td>0.030437</td>\n",
              "      <td>0.168040</td>\n",
              "      <td>0.985294</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hebrew</th>\n",
              "      <td>98</td>\n",
              "      <td>0.046806</td>\n",
              "      <td>0.240491</td>\n",
              "      <td>0.979167</td>\n",
              "      <td>0.992660</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hindi</th>\n",
              "      <td>99</td>\n",
              "      <td>0.038486</td>\n",
              "      <td>0.167708</td>\n",
              "      <td>0.966667</td>\n",
              "      <td>0.998818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>japanese</th>\n",
              "      <td>29</td>\n",
              "      <td>0.674087</td>\n",
              "      <td>53.259259</td>\n",
              "      <td>0.946988</td>\n",
              "      <td>0.034352</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>korean</th>\n",
              "      <td>75</td>\n",
              "      <td>0.173840</td>\n",
              "      <td>0.686620</td>\n",
              "      <td>0.995392</td>\n",
              "      <td>0.448421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latin</th>\n",
              "      <td>100</td>\n",
              "      <td>0.027611</td>\n",
              "      <td>0.169261</td>\n",
              "      <td>0.981132</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spanish</th>\n",
              "      <td>99</td>\n",
              "      <td>0.060906</td>\n",
              "      <td>0.336709</td>\n",
              "      <td>0.880000</td>\n",
              "      <td>0.997281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>thai</th>\n",
              "      <td>59</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>5.142857</td>\n",
              "      <td>0.974684</td>\n",
              "      <td>0.373060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>urdu</th>\n",
              "      <td>96</td>\n",
              "      <td>0.038528</td>\n",
              "      <td>0.153439</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>0.954732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vietnamese</th>\n",
              "      <td>99</td>\n",
              "      <td>0.021310</td>\n",
              "      <td>0.075000</td>\n",
              "      <td>0.989899</td>\n",
              "      <td>0.998874</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 18,\n  \"fields\": [\n    {\n      \"column\": \"Languages\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"arabic\",\n          \"bengali\",\n          \"greek\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 24,\n        \"max\": 100,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          24,\n          75,\n          100\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.19634747342347822,\n        \"min\": 0.020288206295032234,\n        \"max\": 0.674087266251113,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.030927835051546393,\n          0.0354775828460039,\n          0.03043691703485518\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.873922557853087,\n        \"min\": 0.075,\n        \"max\": 53.25925925925926,\n        \"num_unique_values\": 18,\n        \"samples\": [\n          0.15568862275449102,\n          0.1534090909090909,\n          0.16803953871499178\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.02545892435154941,\n        \"min\": 0.88,\n        \"max\": 0.9953917050691244,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          0.98,\n          0.9827586206896551,\n          0.9767441860465116\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.34744659110653675,\n        \"min\": 0.006081103291140964,\n        \"max\": 1.0000000000000036,\n        \"num_unique_values\": 17,\n        \"samples\": [\n          1.0000000000000036,\n          1.0000000000000018,\n          0.9999999999999993\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LRy9xCV6O3Nb"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "def evaluate_ocr_models_for_different_fonts(num_docs, starting_font, increment_font):\n",
        "    ocr_models = {\n",
        "        # \"gemini_flash\": extract_text_gemini,\n",
        "        \"opus\": extract_text_opus,\n",
        "        \"sonnet\": extract_text_sonnet,\n",
        "        \"haiku\": extract_text_haiku,\n",
        "        \"gpt4_turbo\": extract_text_gpt,\n",
        "        \"gpt4o\": extract_text_gpt,\n",
        "        \"vision\": extract_text_vision,\n",
        "        \"tesseract\": extract_text_tesseract,\n",
        "    }\n",
        "\n",
        "    evaluation_metrics = {\n",
        "        \"Fuzzy Score\": calculate_fuzzy_score,\n",
        "        \"CER\": calculate_cer,\n",
        "        \"WER\": calculate_wer,\n",
        "        # \"BLEU\": calculate_bleu,\n",
        "        \"Jaccard Index\": calculate_jaccard_index,\n",
        "        \"Cosine Similarity\": calculate_cosine_similarity\n",
        "    }\n",
        "\n",
        "    results = {model: {metric: [] for metric in evaluation_metrics} for model in ocr_models}\n",
        "\n",
        "    for model in ocr_models:\n",
        "        for docs in range(1, 12):\n",
        "            font_size = docs\n",
        "            model_output_path = f\"/content/{model}_font_output_{font_size}.txt\"\n",
        "            reference_path = f\"/content/font-reference.txt\"\n",
        "\n",
        "            if os.path.exists(model_output_path):\n",
        "                for metric in evaluation_metrics:\n",
        "                    score = evaluation_metrics[metric](read_file(reference_path), read_file(model_output_path))\n",
        "                    results[model][metric].append(score)\n",
        "            else:\n",
        "                for metric in evaluation_metrics:\n",
        "                    print(model_output_path)\n",
        "                    results[model][metric].append(None)\n",
        "\n",
        "        # for docs in range(num_docs):\n",
        "        #     font_size = starting_font + docs * increment_font\n",
        "        #     model_output_path = f\"/content/{model}_font_output_{font_size}.txt\"\n",
        "        #     reference_path = f\"/content/font-reference.txt\"\n",
        "\n",
        "        #     if os.path.exists(model_output_path):\n",
        "        #         for metric in evaluation_metrics:\n",
        "        #             score = evaluation_metrics[metric](read_file(reference_path), read_file(model_output_path))\n",
        "        #             results[model][metric].append(score)\n",
        "        #     else:\n",
        "        #         for metric in evaluation_metrics:\n",
        "        #             print(model_output_path)\n",
        "        #             results[model][metric].append(None)\n",
        "\n",
        "    models_dfs = {}\n",
        "    for model, metrics_scores in results.items():\n",
        "        df = pd.DataFrame(metrics_scores)\n",
        "        # df.index = [docs+1 if docs<11 else (starting_font + (docs-11) * increment_font) for docs in range(len(df))]\n",
        "        df.index = [docs+1 for docs in range(len(df))]\n",
        "        df.index.name = 'Font Size'\n",
        "        models_dfs[model] = df\n",
        "\n",
        "    return models_dfs"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "H0Stery6Q7cu",
        "outputId": "b9c8b11e-b662-4c96-83d4-026f270377aa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/vision_font_output_1.txt\n",
            "/content/vision_font_output_1.txt\n",
            "/content/vision_font_output_1.txt\n",
            "/content/vision_font_output_1.txt\n",
            "/content/vision_font_output_1.txt\n",
            "Model: opus\n"
          ]
        },
        {
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Font Size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          1,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 11,\n        \"max\": 83,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          83,\n          54,\n          81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2706902059592143,\n        \"min\": 0.20877659574468085,\n        \"max\": 0.9401595744680851,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.5332446808510638,\n          0.9391622340425532,\n          0.20877659574468085\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.6112781316192542,\n        \"min\": 1.188976377952756,\n        \"max\": 5.56496062992126,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          3.1003937007874014,\n          5.559055118110236,\n          1.188976377952756\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1278999198278638,\n        \"min\": 0.5777777777777777,\n        \"max\": 0.9148936170212766,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.8541666666666666,\n          0.6461538461538462,\n          0.8333333333333334\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2254302455083485,\n        \"min\": 0.40280813892384887,\n        \"max\": 0.9686748398131518,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.7377493548436327,\n          0.5343168969237784,\n          0.9646151323331051\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
              "      <th>Cosine Similarity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Font Size</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>11</td>\n",
              "      <td>0.939162</td>\n",
              "      <td>5.559055</td>\n",
              "      <td>0.577778</td>\n",
              "      <td>0.534317</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11</td>\n",
              "      <td>0.940160</td>\n",
              "      <td>5.564961</td>\n",
              "      <td>0.577778</td>\n",
              "      <td>0.402808</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>54</td>\n",
              "      <td>0.744016</td>\n",
              "      <td>4.385827</td>\n",
              "      <td>0.646154</td>\n",
              "      <td>0.671715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49</td>\n",
              "      <td>0.659574</td>\n",
              "      <td>3.866142</td>\n",
              "      <td>0.734694</td>\n",
              "      <td>0.411806</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>58</td>\n",
              "      <td>0.545213</td>\n",
              "      <td>3.179134</td>\n",
              "      <td>0.653846</td>\n",
              "      <td>0.713956</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>56</td>\n",
              "      <td>0.533245</td>\n",
              "      <td>3.100394</td>\n",
              "      <td>0.770833</td>\n",
              "      <td>0.737749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>81</td>\n",
              "      <td>0.244681</td>\n",
              "      <td>1.429134</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.957964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>77</td>\n",
              "      <td>0.281250</td>\n",
              "      <td>1.645669</td>\n",
              "      <td>0.914894</td>\n",
              "      <td>0.941545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>81</td>\n",
              "      <td>0.235040</td>\n",
              "      <td>1.364173</td>\n",
              "      <td>0.854167</td>\n",
              "      <td>0.963042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>83</td>\n",
              "      <td>0.208777</td>\n",
              "      <td>1.188976</td>\n",
              "      <td>0.914894</td>\n",
              "      <td>0.964615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>78</td>\n",
              "      <td>0.501995</td>\n",
              "      <td>2.950787</td>\n",
              "      <td>0.860000</td>\n",
              "      <td>0.968675</td>\n",
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              "        document.querySelector('#df-8e5ace3d-5c2e-44bb-9459-1e326547b95d button.colab-df-convert');\n",
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              "      async function convertToInteractive(key) {\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
              "\n",
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ccd8ad70-c802-4937-a832-f756a79ca3ba button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           Fuzzy Score       CER       WER  Jaccard Index  Cosine Similarity\n",
              "Font Size                                                                   \n",
              "1                   11  0.939162  5.559055       0.577778           0.534317\n",
              "2                   11  0.940160  5.564961       0.577778           0.402808\n",
              "3                   54  0.744016  4.385827       0.646154           0.671715\n",
              "4                   49  0.659574  3.866142       0.734694           0.411806\n",
              "5                   58  0.545213  3.179134       0.653846           0.713956\n",
              "6                   56  0.533245  3.100394       0.770833           0.737749\n",
              "7                   81  0.244681  1.429134       0.833333           0.957964\n",
              "8                   77  0.281250  1.645669       0.914894           0.941545\n",
              "9                   81  0.235040  1.364173       0.854167           0.963042\n",
              "10                  83  0.208777  1.188976       0.914894           0.964615\n",
              "11                  78  0.501995  2.950787       0.860000           0.968675"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Model: sonnet\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Font Size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          1,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 13,\n        \"max\": 81,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          43,\n          14,\n          73\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.26048711871268654,\n        \"min\": 0.20944148936170212,\n        \"max\": 0.9305186170212766,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.691156914893617,\n          0.9271941489361702,\n          0.3134973404255319\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5598515803234427,\n        \"min\": 1.218503937007874,\n        \"max\": 5.50984251968504,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          4.057086614173229,\n          5.49015748031496,\n          1.8228346456692914\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07769381021431758,\n        \"min\": 0.6,\n        \"max\": 0.8367346938775511,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.8367346938775511,\n          0.6666666666666666,\n          0.6862745098039216\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3092924518668363,\n        \"min\": 0.13849155265525223,\n        \"max\": 0.964876185576174,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.13849155265525223,\n          0.21952362028805444,\n          0.9251568793533181\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "\n",
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              "\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
              "      <th>Cosine Similarity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Font Size</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14</td>\n",
              "      <td>0.927194</td>\n",
              "      <td>5.490157</td>\n",
              "      <td>0.644444</td>\n",
              "      <td>0.219524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>15</td>\n",
              "      <td>0.915891</td>\n",
              "      <td>5.423228</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.355524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13</td>\n",
              "      <td>0.930519</td>\n",
              "      <td>5.509843</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.404004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>52</td>\n",
              "      <td>0.754654</td>\n",
              "      <td>4.435039</td>\n",
              "      <td>0.633333</td>\n",
              "      <td>0.529081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>53</td>\n",
              "      <td>0.677859</td>\n",
              "      <td>3.944882</td>\n",
              "      <td>0.696429</td>\n",
              "      <td>0.421371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>43</td>\n",
              "      <td>0.691157</td>\n",
              "      <td>4.057087</td>\n",
              "      <td>0.686275</td>\n",
              "      <td>0.138492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>51</td>\n",
              "      <td>0.564495</td>\n",
              "      <td>3.281496</td>\n",
              "      <td>0.700000</td>\n",
              "      <td>0.737316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>69</td>\n",
              "      <td>0.441157</td>\n",
              "      <td>2.555118</td>\n",
              "      <td>0.765957</td>\n",
              "      <td>0.894934</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>75</td>\n",
              "      <td>0.343750</td>\n",
              "      <td>1.944882</td>\n",
              "      <td>0.836735</td>\n",
              "      <td>0.945566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>73</td>\n",
              "      <td>0.313497</td>\n",
              "      <td>1.822835</td>\n",
              "      <td>0.722222</td>\n",
              "      <td>0.925157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>81</td>\n",
              "      <td>0.209441</td>\n",
              "      <td>1.218504</td>\n",
              "      <td>0.836735</td>\n",
              "      <td>0.964876</td>\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    })();\n",
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            "text/plain": [
              "           Fuzzy Score       CER       WER  Jaccard Index  Cosine Similarity\n",
              "Font Size                                                                   \n",
              "1                   14  0.927194  5.490157       0.644444           0.219524\n",
              "2                   15  0.915891  5.423228       0.666667           0.355524\n",
              "3                   13  0.930519  5.509843       0.600000           0.404004\n",
              "4                   52  0.754654  4.435039       0.633333           0.529081\n",
              "5                   53  0.677859  3.944882       0.696429           0.421371\n",
              "6                   43  0.691157  4.057087       0.686275           0.138492\n",
              "7                   51  0.564495  3.281496       0.700000           0.737316\n",
              "8                   69  0.441157  2.555118       0.765957           0.894934\n",
              "9                   75  0.343750  1.944882       0.836735           0.945566\n",
              "10                  73  0.313497  1.822835       0.722222           0.925157\n",
              "11                  81  0.209441  1.218504       0.836735           0.964876"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Model: haiku\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Font Size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          1,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 7,\n        \"max\": 88,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          65,\n          9,\n          77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2583607660330754,\n        \"min\": 0.20079787234042554,\n        \"max\": 0.961436170212766,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.5728058510638298,\n          0.953125,\n          0.3949468085106383\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5375984125960058,\n        \"min\": 1.1751968503937007,\n        \"max\": 5.692913385826771,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          3.358267716535433,\n          5.643700787401575,\n          2.3188976377952755\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11353233506269551,\n        \"min\": 0.5111111111111111,\n        \"max\": 0.875,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          0.8571428571428571,\n          0.5777777777777777,\n          0.6222222222222222\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.27283869866102467,\n        \"min\": 0.23374491359409186,\n        \"max\": 0.9838809725569844,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.8651734765851289,\n          0.23374491359409186,\n          0.9531800031135179\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-23093112-463e-490a-9acf-83a95da2e9ba\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
              "      <th>Cosine Similarity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Font Size</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9</td>\n",
              "      <td>0.953125</td>\n",
              "      <td>5.643701</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.233745</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>0.955785</td>\n",
              "      <td>5.657480</td>\n",
              "      <td>0.577778</td>\n",
              "      <td>0.318580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>0.961436</td>\n",
              "      <td>5.692913</td>\n",
              "      <td>0.511111</td>\n",
              "      <td>0.398063</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13</td>\n",
              "      <td>0.927859</td>\n",
              "      <td>5.494094</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.428811</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>16</td>\n",
              "      <td>0.911902</td>\n",
              "      <td>5.397638</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.451232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>65</td>\n",
              "      <td>0.572806</td>\n",
              "      <td>3.358268</td>\n",
              "      <td>0.735849</td>\n",
              "      <td>0.865173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>25</td>\n",
              "      <td>0.857048</td>\n",
              "      <td>5.072835</td>\n",
              "      <td>0.622222</td>\n",
              "      <td>0.592152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>28</td>\n",
              "      <td>0.841755</td>\n",
              "      <td>4.984252</td>\n",
              "      <td>0.630435</td>\n",
              "      <td>0.747555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>49</td>\n",
              "      <td>0.700465</td>\n",
              "      <td>4.141732</td>\n",
              "      <td>0.680851</td>\n",
              "      <td>0.883847</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>77</td>\n",
              "      <td>0.394947</td>\n",
              "      <td>2.318898</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.953180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>88</td>\n",
              "      <td>0.200798</td>\n",
              "      <td>1.175197</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.983881</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23093112-463e-490a-9acf-83a95da2e9ba')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-23093112-463e-490a-9acf-83a95da2e9ba button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-23093112-463e-490a-9acf-83a95da2e9ba');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8efadecb-06cc-4b1d-8c67-497f4589e729 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           Fuzzy Score       CER       WER  Jaccard Index  Cosine Similarity\n",
              "Font Size                                                                   \n",
              "1                    9  0.953125  5.643701       0.600000           0.233745\n",
              "2                    8  0.955785  5.657480       0.577778           0.318580\n",
              "3                    7  0.961436  5.692913       0.511111           0.398063\n",
              "4                   13  0.927859  5.494094       0.666667           0.428811\n",
              "5                   16  0.911902  5.397638       0.600000           0.451232\n",
              "6                   65  0.572806  3.358268       0.735849           0.865173\n",
              "7                   25  0.857048  5.072835       0.622222           0.592152\n",
              "8                   28  0.841755  4.984252       0.630435           0.747555\n",
              "9                   49  0.700465  4.141732       0.680851           0.883847\n",
              "10                  77  0.394947  2.318898       0.857143           0.953180\n",
              "11                  88  0.200798  1.175197       0.875000           0.983881"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Model: gpt4_turbo\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Font Size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          1,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 6,\n        \"max\": 96,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          60,\n          6,\n          94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3421238782483519,\n        \"min\": 0.04920212765957447,\n        \"max\": 0.9654255319148937,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.6449468085106383,\n          0.9654255319148937,\n          0.07912234042553191\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.028721720144799,\n        \"min\": 0.2755905511811024,\n        \"max\": 5.716535433070866,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          3.765748031496063,\n          5.716535433070866,\n          0.44291338582677164\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.11537976396364674,\n        \"min\": 0.5333333333333333,\n        \"max\": 0.9148936170212766,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.7647058823529411,\n          0.5333333333333333,\n          0.86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25606447753978095,\n        \"min\": 0.2987571711162542,\n        \"max\": 0.9888192836918042,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.7837497701197942,\n          0.3718509279635078,\n          0.9888192836918042\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-64539ae8-2e95-4cf3-9b4d-ded47a457250\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
              "      <th>Cosine Similarity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Font Size</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>0.965426</td>\n",
              "      <td>5.716535</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.371851</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13</td>\n",
              "      <td>0.927194</td>\n",
              "      <td>5.490157</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.298757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>26</td>\n",
              "      <td>0.850731</td>\n",
              "      <td>5.029528</td>\n",
              "      <td>0.641509</td>\n",
              "      <td>0.548370</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>53</td>\n",
              "      <td>0.672872</td>\n",
              "      <td>3.944882</td>\n",
              "      <td>0.692308</td>\n",
              "      <td>0.581897</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>56</td>\n",
              "      <td>0.707114</td>\n",
              "      <td>4.131890</td>\n",
              "      <td>0.735849</td>\n",
              "      <td>0.666032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>60</td>\n",
              "      <td>0.644947</td>\n",
              "      <td>3.765748</td>\n",
              "      <td>0.764706</td>\n",
              "      <td>0.783750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>71</td>\n",
              "      <td>0.413231</td>\n",
              "      <td>2.427165</td>\n",
              "      <td>0.788462</td>\n",
              "      <td>0.912369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>75</td>\n",
              "      <td>0.420878</td>\n",
              "      <td>2.456693</td>\n",
              "      <td>0.792453</td>\n",
              "      <td>0.945860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>92</td>\n",
              "      <td>0.088098</td>\n",
              "      <td>0.501969</td>\n",
              "      <td>0.895833</td>\n",
              "      <td>0.988406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>94</td>\n",
              "      <td>0.079122</td>\n",
              "      <td>0.442913</td>\n",
              "      <td>0.860000</td>\n",
              "      <td>0.988819</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>96</td>\n",
              "      <td>0.049202</td>\n",
              "      <td>0.275591</td>\n",
              "      <td>0.914894</td>\n",
              "      <td>0.987096</td>\n",
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              "    </button>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-64539ae8-2e95-4cf3-9b4d-ded47a457250');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-ca4549cb-a984-43c8-a38d-90e65ab7510f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ca4549cb-a984-43c8-a38d-90e65ab7510f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ca4549cb-a984-43c8-a38d-90e65ab7510f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           Fuzzy Score       CER       WER  Jaccard Index  Cosine Similarity\n",
              "Font Size                                                                   \n",
              "1                    6  0.965426  5.716535       0.533333           0.371851\n",
              "2                   13  0.927194  5.490157       0.666667           0.298757\n",
              "3                   26  0.850731  5.029528       0.641509           0.548370\n",
              "4                   53  0.672872  3.944882       0.692308           0.581897\n",
              "5                   56  0.707114  4.131890       0.735849           0.666032\n",
              "6                   60  0.644947  3.765748       0.764706           0.783750\n",
              "7                   71  0.413231  2.427165       0.788462           0.912369\n",
              "8                   75  0.420878  2.456693       0.792453           0.945860\n",
              "9                   92  0.088098  0.501969       0.895833           0.988406\n",
              "10                  94  0.079122  0.442913       0.860000           0.988819\n",
              "11                  96  0.049202  0.275591       0.914894           0.987096"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Model: gpt4o\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Font Size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          1,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 7,\n        \"max\": 99,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          98,\n          52,\n          80\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.35695772268159165,\n        \"min\": 0.014960106382978724,\n        \"max\": 0.9647606382978723,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.2509973404255319,\n          0.9647606382978723,\n          0.02759308510638298\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.1139951323900963,\n        \"min\": 0.0531496062992126,\n        \"max\": 5.71259842519685,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          1.4625984251968505,\n          5.71259842519685,\n          0.14566929133858267\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1337860161791627,\n        \"min\": 0.4666666666666667,\n        \"max\": 0.9166666666666666,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.8235294117647058,\n          0.4666666666666667,\n          0.9166666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.25121656836400763,\n        \"min\": 0.31970530939306635,\n        \"max\": 0.9979545301643027,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          0.956832943133306,\n          0.31970530939306635,\n          0.9978873363758954\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-e3975efd-a1a7-449a-995b-35670e8e567c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
              "      <th>Cosine Similarity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Font Size</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7</td>\n",
              "      <td>0.964761</td>\n",
              "      <td>5.712598</td>\n",
              "      <td>0.466667</td>\n",
              "      <td>0.319705</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>52</td>\n",
              "      <td>0.763298</td>\n",
              "      <td>4.507874</td>\n",
              "      <td>0.722222</td>\n",
              "      <td>0.487974</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>51</td>\n",
              "      <td>0.812168</td>\n",
              "      <td>4.748031</td>\n",
              "      <td>0.690909</td>\n",
              "      <td>0.517332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>63</td>\n",
              "      <td>0.647939</td>\n",
              "      <td>3.812992</td>\n",
              "      <td>0.725490</td>\n",
              "      <td>0.850500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>71</td>\n",
              "      <td>0.448138</td>\n",
              "      <td>2.488189</td>\n",
              "      <td>0.754717</td>\n",
              "      <td>0.917159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>80</td>\n",
              "      <td>0.250997</td>\n",
              "      <td>1.462598</td>\n",
              "      <td>0.823529</td>\n",
              "      <td>0.956833</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>87</td>\n",
              "      <td>0.188497</td>\n",
              "      <td>1.090551</td>\n",
              "      <td>0.843137</td>\n",
              "      <td>0.979915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>94</td>\n",
              "      <td>0.088763</td>\n",
              "      <td>0.494094</td>\n",
              "      <td>0.877551</td>\n",
              "      <td>0.991400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>98</td>\n",
              "      <td>0.023936</td>\n",
              "      <td>0.114173</td>\n",
              "      <td>0.914894</td>\n",
              "      <td>0.997767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>99</td>\n",
              "      <td>0.027593</td>\n",
              "      <td>0.145669</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.997887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>99</td>\n",
              "      <td>0.014960</td>\n",
              "      <td>0.053150</td>\n",
              "      <td>0.897959</td>\n",
              "      <td>0.997955</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3975efd-a1a7-449a-995b-35670e8e567c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e3975efd-a1a7-449a-995b-35670e8e567c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-e3975efd-a1a7-449a-995b-35670e8e567c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7210a603-51ea-4093-91ed-702a0e5a3a37\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7210a603-51ea-4093-91ed-702a0e5a3a37')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7210a603-51ea-4093-91ed-702a0e5a3a37 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           Fuzzy Score       CER       WER  Jaccard Index  Cosine Similarity\n",
              "Font Size                                                                   \n",
              "1                    7  0.964761  5.712598       0.466667           0.319705\n",
              "2                   52  0.763298  4.507874       0.722222           0.487974\n",
              "3                   51  0.812168  4.748031       0.690909           0.517332\n",
              "4                   63  0.647939  3.812992       0.725490           0.850500\n",
              "5                   71  0.448138  2.488189       0.754717           0.917159\n",
              "6                   80  0.250997  1.462598       0.823529           0.956833\n",
              "7                   87  0.188497  1.090551       0.843137           0.979915\n",
              "8                   94  0.088763  0.494094       0.877551           0.991400\n",
              "9                   98  0.023936  0.114173       0.914894           0.997767\n",
              "10                  99  0.027593  0.145669       0.916667           0.997887\n",
              "11                  99  0.014960  0.053150       0.897959           0.997955"
            ]
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          "text": [
            "\n",
            "\n",
            "Model: vision\n"
          ]
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              "\n",
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              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>11.0</td>\n",
              "      <td>0.936835</td>\n",
              "      <td>5.547244</td>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.634536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>73.0</td>\n",
              "      <td>0.316157</td>\n",
              "      <td>1.860236</td>\n",
              "      <td>0.706897</td>\n",
              "      <td>0.913670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>95.0</td>\n",
              "      <td>0.081782</td>\n",
              "      <td>0.464567</td>\n",
              "      <td>0.955556</td>\n",
              "      <td>0.982378</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>99.0</td>\n",
              "      <td>0.042221</td>\n",
              "      <td>0.230315</td>\n",
              "      <td>0.955556</td>\n",
              "      <td>0.995717</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.042886</td>\n",
              "      <td>0.234252</td>\n",
              "      <td>0.955556</td>\n",
              "      <td>0.998699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.040559</td>\n",
              "      <td>0.218504</td>\n",
              "      <td>0.955556</td>\n",
              "      <td>0.999907</td>\n",
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              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.045213</td>\n",
              "      <td>0.248031</td>\n",
              "      <td>0.955556</td>\n",
              "      <td>0.999907</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>100.0</td>\n",
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              "      <td>0.206693</td>\n",
              "      <td>0.977778</td>\n",
              "      <td>0.999907</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.069481</td>\n",
              "      <td>0.391732</td>\n",
              "      <td>0.914894</td>\n",
              "      <td>0.999722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>100.0</td>\n",
              "      <td>0.048870</td>\n",
              "      <td>0.267717</td>\n",
              "      <td>0.914894</td>\n",
              "      <td>0.999722</td>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\n",
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              "        document.querySelector('#df-25154530-c02f-4ef2-bf1b-5071925e6320 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-25154530-c02f-4ef2-bf1b-5071925e6320');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d232c16c-8a74-4173-a00f-6f07ebb6b803')\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d232c16c-8a74-4173-a00f-6f07ebb6b803 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "           Fuzzy Score       CER       WER  Jaccard Index  Cosine Similarity\n",
              "Font Size                                                                   \n",
              "1                  NaN       NaN       NaN            NaN                NaN\n",
              "2                 11.0  0.936835  5.547244       0.533333           0.634536\n",
              "3                 73.0  0.316157  1.860236       0.706897           0.913670\n",
              "4                 95.0  0.081782  0.464567       0.955556           0.982378\n",
              "5                 99.0  0.042221  0.230315       0.955556           0.995717\n",
              "6                100.0  0.042886  0.234252       0.955556           0.998699\n",
              "7                100.0  0.040559  0.218504       0.955556           0.999907\n",
              "8                100.0  0.045213  0.248031       0.955556           0.999907\n",
              "9                100.0  0.038564  0.206693       0.977778           0.999907\n",
              "10               100.0  0.069481  0.391732       0.914894           0.999722\n",
              "11               100.0  0.048870  0.267717       0.914894           0.999722"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n",
            "Model: tesseract\n"
          ]
        },
        {
          "data": {
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Font Size\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 1,\n        \"max\": 11,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          6,\n          1,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fuzzy Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 0,\n        \"max\": 65,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          9,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"CER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2380588349780737,\n        \"min\": 0.37333776595744683,\n        \"max\": 0.9996675531914894,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.9996675531914894,\n          0.9544547872340425,\n          0.4328457446808511\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"WER\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.4439344346996803,\n        \"min\": 2.12007874015748,\n        \"max\": 5.921259842519685,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5.921259842519685,\n          5.6515748031496065,\n          2.486220472440945\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jaccard Index\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3133414775340051,\n        \"min\": 0.022222222222222223,\n        \"max\": 0.7017543859649122,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.022222222222222223,\n          0.48936170212765956,\n          0.6666666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cosine Similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3095614211412481,\n        \"min\": 0.0,\n        \"max\": 0.8153483295215893,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0.0,\n          0.058424057987394716,\n          0.7149728391186198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe",
              "variable_name": "df"
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            "text/html": [
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              "  <div id=\"df-467cfcb5-b1ee-4962-b87f-14fe048546b6\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
              "      <th>WER</th>\n",
              "      <th>Jaccard Index</th>\n",
              "      <th>Cosine Similarity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Font Size</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0.999668</td>\n",
              "      <td>5.921260</td>\n",
              "      <td>0.022222</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0.999668</td>\n",
              "      <td>5.921260</td>\n",
              "      <td>0.022222</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0.999668</td>\n",
              "      <td>5.921260</td>\n",
              "      <td>0.022222</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>0.022222</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0</td>\n",
              "      <td>0.999668</td>\n",
              "      <td>5.921260</td>\n",
              "      <td>0.022222</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>9</td>\n",
              "      <td>0.954455</td>\n",
              "      <td>5.651575</td>\n",
              "      <td>0.489362</td>\n",
              "      <td>0.058424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>19</td>\n",
              "      <td>0.896277</td>\n",
              "      <td>5.291339</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.169130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>30</td>\n",
              "      <td>0.833112</td>\n",
              "      <td>4.889764</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.157488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>51</td>\n",
              "      <td>0.638630</td>\n",
              "      <td>3.732283</td>\n",
              "      <td>0.650000</td>\n",
              "      <td>0.510251</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>60</td>\n",
              "      <td>0.432846</td>\n",
              "      <td>2.486220</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.714973</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>65</td>\n",
              "      <td>0.373338</td>\n",
              "      <td>2.120079</td>\n",
              "      <td>0.701754</td>\n",
              "      <td>0.815348</td>\n",
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              "        const element = document.querySelector('#df-467cfcb5-b1ee-4962-b87f-14fe048546b6');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "  }\n",
              "\n",
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              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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            ],
            "text/plain": [
              "           Fuzzy Score       CER       WER  Jaccard Index  Cosine Similarity\n",
              "Font Size                                                                   \n",
              "1                    0  0.999668  5.921260       0.022222           0.000000\n",
              "2                    0  0.999668  5.921260       0.022222           0.000000\n",
              "3                    0  0.999668  5.921260       0.022222           0.000000\n",
              "4                    0  0.999668  5.921260       0.022222           0.000000\n",
              "5                    0  0.999668  5.921260       0.022222           0.000000\n",
              "6                    9  0.954455  5.651575       0.489362           0.058424\n",
              "7                   19  0.896277  5.291339       0.571429           0.169130\n",
              "8                   30  0.833112  4.889764       0.600000           0.157488\n",
              "9                   51  0.638630  3.732283       0.650000           0.510251\n",
              "10                  60  0.432846  2.486220       0.666667           0.714973\n",
              "11                  65  0.373338  2.120079       0.701754           0.815348"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "num_docs = 9\n",
        "evaluation_results = evaluate_ocr_models_for_different_fonts(num_docs, starting_font, increment_font)\n",
        "\n",
        "for model, df in evaluation_results.items():\n",
        "    print(f\"Model: {model}\")\n",
        "    display(df)\n",
        "    print(\"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eOfF1FQszIqj"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_evaluation_metric_for_all_models(models_dfs, evaluation_metric):\n",
        "    fig, axs = plt.subplots(4, 2, figsize=(20, 20))  # Create a 3x2 grid of subplots\n",
        "    fig.suptitle(f'{evaluation_metric} for Different OCR Models', fontsize=16)\n",
        "\n",
        "    models = list(models_dfs.keys())\n",
        "\n",
        "    for i, model in enumerate(models):\n",
        "        row = i // 2\n",
        "        col = i % 2\n",
        "        ax = axs[row, col]\n",
        "\n",
        "        df = models_dfs[model]\n",
        "\n",
        "        if evaluation_metric in df.columns:\n",
        "            ax.bar(df.index, df[evaluation_metric], color=\"#d4377c\")\n",
        "            ax.set_title(model)\n",
        "            ax.set_xlabel('Font Size')\n",
        "            ax.set_ylabel(evaluation_metric)\n",
        "\n",
        "        else:\n",
        "            ax.text(0.5, 0.5, 'Metric Not Available', horizontalalignment='center', verticalalignment='center', transform=ax.transAxes)\n",
        "            ax.set_title(model)\n",
        "            ax.set_xlabel('Font Size')\n",
        "            ax.set_ylabel(evaluation_metric)\n",
        "\n",
        "    for j in range(len(models), 6):\n",
        "        row = j // 2\n",
        "        col = j % 2\n",
        "        fig.delaxes(axs[row, col])\n",
        "\n",
        "    plt.tight_layout(rect=[0, 0.03, 1, 0.95])  # Adjust layout to fit title\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Sis7oGDkz6RR",
        "outputId": "fd6e8b1a-5e84-4bf5-ba0e-6b70e3a3900e"
      },
      "outputs": [
        {
          "data": {
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            "text/plain": [
              "<Figure size 2000x2000 with 8 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_evaluation_metric_for_all_models(evaluation_results, 'Cosine Similarity')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D9GJEfL8wjRw"
      },
      "outputs": [],
      "source": [
        "evaluation_metrics = {\n",
        "    \"Fuzzy Score\": calculate_fuzzy_score,\n",
        "    \"CER\": calculate_cer,\n",
        "    \"WER\": calculate_wer,\n",
        "    \"BLEU\": calculate_bleu,\n",
        "    \"Jaccard Index\": calculate_jaccard_index,\n",
        "    \"Cosine Similarity\": calculate_cosine_similarity\n",
        "}\n",
        "\n",
        "# plot_line_graphs(evaluation_results, evaluation_metrics)\n",
        "# plot_heatmaps(evaluation_results, evaluation_metrics)\n",
        "# plot_box_plots(evaluation_results, evaluation_metrics)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        },
        "id": "YqLAlGsHo8HV",
        "outputId": "62cb5c2a-dedf-44f0-9d2d-c89344b86808"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
            "The hypothesis contains 0 counts of 3-gram overlaps.\n",
            "Therefore the BLEU score evaluates to 0, independently of\n",
            "how many N-gram overlaps of lower order it contains.\n",
            "Consider using lower n-gram order or use SmoothingFunction()\n",
            "  warnings.warn(_msg)\n",
            "/usr/local/lib/python3.10/dist-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
            "The hypothesis contains 0 counts of 4-gram overlaps.\n",
            "Therefore the BLEU score evaluates to 0, independently of\n",
            "how many N-gram overlaps of lower order it contains.\n",
            "Consider using lower n-gram order or use SmoothingFunction()\n",
            "  warnings.warn(_msg)\n",
            "/usr/local/lib/python3.10/dist-packages/nltk/translate/bleu_score.py:552: UserWarning: \n",
            "The hypothesis contains 0 counts of 2-gram overlaps.\n",
            "Therefore the BLEU score evaluates to 0, independently of\n",
            "how many N-gram overlaps of lower order it contains.\n",
            "Consider using lower n-gram order or use SmoothingFunction()\n",
            "  warnings.warn(_msg)\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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              "      <th>Fuzzy Score</th>\n",
              "      <th>CER</th>\n",
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              "      <th>BLEU</th>\n",
              "      <th>Jaccard Index</th>\n",
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              "      <th>Models</th>\n",
              "      <th></th>\n",
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              "      <th>gemini_pro</th>\n",
              "      <td>91.400000</td>\n",
              "      <td>0.208400</td>\n",
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              "      <td>0.706196</td>\n",
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              "      <th>gemini_flash</th>\n",
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              "      <th>opus</th>\n",
              "      <td>95.666667</td>\n",
              "      <td>0.087719</td>\n",
              "      <td>0.469858</td>\n",
              "      <td>0.818859</td>\n",
              "      <td>0.930505</td>\n",
              "      <td>0.983913</td>\n",
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              "    <tr>\n",
              "      <th>sonnet</th>\n",
              "      <td>73.933333</td>\n",
              "      <td>0.370517</td>\n",
              "      <td>2.215236</td>\n",
              "      <td>0.543590</td>\n",
              "      <td>0.763340</td>\n",
              "      <td>0.741126</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>haiku</th>\n",
              "      <td>87.666667</td>\n",
              "      <td>0.209490</td>\n",
              "      <td>1.194556</td>\n",
              "      <td>0.679309</td>\n",
              "      <td>0.886219</td>\n",
              "      <td>0.951519</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gpt4_turbo</th>\n",
              "      <td>96.200000</td>\n",
              "      <td>0.099277</td>\n",
              "      <td>0.552344</td>\n",
              "      <td>0.832023</td>\n",
              "      <td>0.906389</td>\n",
              "      <td>0.980501</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gpt4o</th>\n",
              "      <td>97.666667</td>\n",
              "      <td>0.101787</td>\n",
              "      <td>0.375122</td>\n",
              "      <td>0.885050</td>\n",
              "      <td>0.914903</td>\n",
              "      <td>0.990599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>vision</th>\n",
              "      <td>99.600000</td>\n",
              "      <td>0.081458</td>\n",
              "      <td>0.414169</td>\n",
              "      <td>0.514288</td>\n",
              "      <td>0.930115</td>\n",
              "      <td>0.997395</td>\n",
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              "  </tbody>\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        document.querySelector('#df-dfe3167c-1dd4-4dee-bca2-43f036ee7a4a button.colab-df-convert');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-50ca3a57-695b-4e90-bef3-b2af8218e334 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "              Fuzzy Score       CER       WER      BLEU  Jaccard Index  \\\n",
              "Models                                                                   \n",
              "gemini_pro      91.400000  0.208400  1.112080  0.706196       0.919495   \n",
              "gemini_flash    51.800000  0.620127  3.590325  0.227584       0.823805   \n",
              "opus            95.666667  0.087719  0.469858  0.818859       0.930505   \n",
              "sonnet          73.933333  0.370517  2.215236  0.543590       0.763340   \n",
              "haiku           87.666667  0.209490  1.194556  0.679309       0.886219   \n",
              "gpt4_turbo      96.200000  0.099277  0.552344  0.832023       0.906389   \n",
              "gpt4o           97.666667  0.101787  0.375122  0.885050       0.914903   \n",
              "vision          99.600000  0.081458  0.414169  0.514288       0.930115   \n",
              "\n",
              "              Cosine Similarity  \n",
              "Models                           \n",
              "gemini_pro             0.974929  \n",
              "gemini_flash           0.872291  \n",
              "opus                   0.983913  \n",
              "sonnet                 0.741126  \n",
              "haiku                  0.951519  \n",
              "gpt4_turbo             0.980501  \n",
              "gpt4o                  0.990599  \n",
              "vision                 0.997395  "
            ]
          },
          "execution_count": 133,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "evaluate_ocr_models(9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "IG2BEKxmkFgV"
      },
      "outputs": [],
      "source": [
        "languages = [\"arabic\", \"bengali\", \"chinese\", \"cyrillic\", \"dutch\", \"english\", \"french\", \"german\", \"greek\", \"hebrew\", \"hindi\", \"japanese\", \"korean\", \"latin\", \"spanish\", \"thai\", \"urdu\", \"vietnamese\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqvwcOYyw9H0",
        "outputId": "773fcde2-8b58-495d-e394-384f36106d2b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "18"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(languages)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CQlOQ5gG7orW"
      },
      "source": [
        "## **Marker**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g973ztuocjjG"
      },
      "outputs": [],
      "source": [
        "!pip install marker-pdf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1EM1nFCQfSFy"
      },
      "outputs": [],
      "source": [
        "!pip install poetry"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kSOPEJ57d2fL",
        "outputId": "fc5eb94a-3575-408c-e67c-6b639bb3e113"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'marker'...\n",
            "remote: Enumerating objects: 1177, done.\u001b[K\n",
            "remote: Counting objects: 100% (530/530), done.\u001b[K\n",
            "remote: Compressing objects: 100% (163/163), done.\u001b[K\n",
            "remote: Total 1177 (delta 421), reused 431 (delta 360), pack-reused 647\u001b[K\n",
            "Receiving objects: 100% (1177/1177), 1.41 MiB | 7.03 MiB/s, done.\n",
            "Resolving deltas: 100% (774/774), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/VikParuchuri/marker.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SutZucw-fOn5"
      },
      "outputs": [],
      "source": [
        "%cd /content/marker\n",
        "!poetry install"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kc_mWIrVmgrV"
      },
      "outputs": [],
      "source": [
        "!marker_single /content/scan1.pdf /content --batch_multiplier 2 --max_pages 10 --langs English"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUYM2Ovn7tW9"
      },
      "source": [
        "# **Claude**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d44IMxaC84bG"
      },
      "outputs": [],
      "source": [
        "!pip install anthropic\n",
        "!pip install fitz\n",
        "!pip install PyMuPDF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bdakn4AIt2Cj"
      },
      "outputs": [],
      "source": [
        "import fitz  # PyMuPDF\n",
        "import anthropic\n",
        "import base64\n",
        "import httpx\n",
        "from PIL import Image\n",
        "import requests"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2ZYLwD_Bt4JB"
      },
      "outputs": [],
      "source": [
        "image_media_type = \"image/png\"\n",
        "client = anthropic.Anthropic(\n",
        "    api_key = \"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\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "509VKaGm8z_L"
      },
      "outputs": [],
      "source": [
        "def convert_pdf_to_images(pdf_path):\n",
        "    images = []\n",
        "    with fitz.open(pdf_path) as doc:\n",
        "        for page_num in range(len(doc)):\n",
        "            page = doc.load_page(page_num)\n",
        "            pix = page.get_pixmap()\n",
        "            images.append(pix)\n",
        "    return images\n",
        "\n",
        "def encode_image_to_base64(image):\n",
        "    image_bytes = image.tobytes()\n",
        "    base64_encoded = base64.b64encode(image_bytes)\n",
        "    base64_string = base64_encoded.decode(\"utf-8\")\n",
        "    return base64_string"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SqPIotb99sLB"
      },
      "source": [
        "**Claude - Opus**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eDM51V2Ql0av"
      },
      "outputs": [],
      "source": [
        "def extract_text_opus(pdf_path):\n",
        "    images = convert_pdf_to_images(pdf_path)\n",
        "    extracted_text = \"\"\n",
        "    for image in images:\n",
        "        base64_string = encode_image_to_base64(image)\n",
        "        message = client.messages.create(\n",
        "            model=\"claude-3-opus-20240229\",\n",
        "            max_tokens=1024,\n",
        "            messages=[\n",
        "                {\n",
        "                    \"role\": \"user\",\n",
        "                    \"content\": [\n",
        "                        {\n",
        "                            \"type\": \"image\",\n",
        "                            \"source\": {\n",
        "                                \"type\": \"base64\",\n",
        "                                \"media_type\": image_media_type,\n",
        "                                \"data\": base64_string,\n",
        "                            },\n",
        "                        },\n",
        "                        {\n",
        "                            \"type\": \"text\",\n",
        "                            \"text\": \"Return the bounding boxes of all the tables present in this image.\"\n",
        "                        }\n",
        "                    ],\n",
        "                }\n",
        "            ],\n",
        "        )\n",
        "        extracted_text += message.content[0].text\n",
        "    return extracted_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SdGdnz_NvK7K"
      },
      "outputs": [],
      "source": [
        "num_docs = 11\n",
        "for language in range(len(languages)):\n",
        "    extracted_text = extract_text_opus((\"/content/{}.pdf\").format(languages[language]))\n",
        "    output_file_path = (\"/content/opus_{}_output.txt\").format(languages[language])\n",
        "    with open(output_file_path, \"w\") as txt_file:\n",
        "        txt_file.write(extracted_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9OtYd0SG98BY"
      },
      "source": [
        "**Claude - Sonnet**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3319Um258rJk"
      },
      "outputs": [],
      "source": [
        "def extract_text_sonnet(pdf_path):\n",
        "    images = convert_pdf_to_images(pdf_path)\n",
        "    extracted_text = \"\"\n",
        "    for image in images:\n",
        "        base64_string = encode_image_to_base64(image)\n",
        "        message = client.messages.create(\n",
        "            model=\"claude-3-sonnet-20240229\",\n",
        "            max_tokens=1024,\n",
        "            messages=[\n",
        "                {\n",
        "                    \"role\": \"user\",\n",
        "                    \"content\": [\n",
        "                        {\n",
        "                            \"type\": \"image\",\n",
        "                            \"source\": {\n",
        "                                \"type\": \"base64\",\n",
        "                                \"media_type\": image_media_type,\n",
        "                                \"data\": base64_string,\n",
        "                            },\n",
        "                        },\n",
        "                        {\n",
        "                            \"type\": \"text\",\n",
        "                            \"text\": \"Extract the actual text from this image and don't summarize.\"\n",
        "                        }\n",
        "                    ],\n",
        "                }\n",
        "            ],\n",
        "        )\n",
        "        extracted_text += message.content[0].text\n",
        "    return extracted_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ODh6BiUL9VcD"
      },
      "outputs": [],
      "source": [
        "for language in range(len(languages)):\n",
        "    extracted_text = extract_text_sonnet((\"/content/{}.pdf\").format(languages[language]))\n",
        "    output_file_path = (\"/content/sonnet_{}_output.txt\").format(languages[language])\n",
        "    with open(output_file_path, \"w\") as txt_file:\n",
        "        txt_file.write(extracted_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CThXxO7p-BMJ"
      },
      "source": [
        "**Claude - Haiku**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "L541KQ9J9Lvy"
      },
      "outputs": [],
      "source": [
        "def extract_text_haiku(pdf_path):\n",
        "    images = convert_pdf_to_images(pdf_path)\n",
        "    extracted_text = \"\"\n",
        "    for image in images:\n",
        "        base64_string = encode_image_to_base64(image)\n",
        "        message = client.messages.create(\n",
        "            model=\"claude-3-haiku-20240307\",\n",
        "            max_tokens=1024,\n",
        "            messages=[\n",
        "                {\n",
        "                    \"role\": \"user\",\n",
        "                    \"content\": [\n",
        "                        {\n",
        "                            \"type\": \"image\",\n",
        "                            \"source\": {\n",
        "                                \"type\": \"base64\",\n",
        "                                \"media_type\": image_media_type,\n",
        "                                \"data\": base64_string,\n",
        "                            },\n",
        "                        },\n",
        "                        {\n",
        "                            \"type\": \"text\",\n",
        "                            \"text\": \"Extract the text from this image.\"\n",
        "                        }\n",
        "                    ],\n",
        "                }\n",
        "            ],\n",
        "        )\n",
        "        extracted_text += message.content[0].text\n",
        "    return extracted_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E-99_Lnj9cJD"
      },
      "outputs": [],
      "source": [
        "for language in range(len(languages)):\n",
        "    extracted_text = extract_text_haiku((\"/content/{}.pdf\").format(languages[language]))\n",
        "    output_file_path = (\"/content/haiku_{}_output.txt\").format(languages[language])\n",
        "    with open(output_file_path, \"w\") as txt_file:\n",
        "        txt_file.write(extracted_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gfKCNSmLyW5f"
      },
      "source": [
        "# **Gemini**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iY_ykg3GzNDH"
      },
      "outputs": [],
      "source": [
        "!pip install google-generativeai\n",
        "!pip install fitz\n",
        "!pip install PyMuPDF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2BP_rT0vAz1S"
      },
      "outputs": [],
      "source": [
        "import fitz  # PyMuPDF\n",
        "import google.generativeai as genai\n",
        "import os\n",
        "import pathlib\n",
        "import PIL.Image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HTnd3kkkA2IP"
      },
      "outputs": [],
      "source": [
        "num_docs = 4\n",
        "genai.configure(api_key= 'AIzaSyBwk94xRhPOIkvO0E3pYhXQ7Rrk5my5IyY')\n",
        "gemini_pro_vision = genai.GenerativeModel('gemini-pro-vision')\n",
        "genimi_gemini_flash = genai.GenerativeModel('gemini-1.5-flash-latest')\n",
        "prompt = \"Extract the text from this image.\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kcZrMxbj-I2c"
      },
      "outputs": [],
      "source": [
        "def extract_text_gemini(model, pdf_path):\n",
        "    model = genai.GenerativeModel('gemini-1.5-flash-latest')\n",
        "    extracted_text = \"\"\n",
        "    with fitz.open(pdf_path) as doc:\n",
        "        for page_num in range(len(doc)):\n",
        "            page = doc.load_page(page_num)\n",
        "            pix = page.get_pixmap()\n",
        "            img = PIL.Image.frombytes(\"RGB\", [pix.width, pix.height], pix.samples)\n",
        "            response = model.generate_content(\n",
        "                [img, prompt], stream=False\n",
        "            )\n",
        "            response.resolve()\n",
        "            extracted_text += response.text\n",
        "            print(extracted_text)\n",
        "        return extracted_text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5XR-W_cR3vBw"
      },
      "source": [
        "**Gemini 1.5 Pro**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ir2GgoHk17AK"
      },
      "outputs": [],
      "source": [
        "num_docs = 9\n",
        "for language in range(len(languages)):\n",
        "    print(languages[language])\n",
        "    extracted_text = extract_text_gemini(gemini_pro_vision, (\"/content/{}.pdf\").format(languages[language]))\n",
        "    output_file_path = (\"/content/gemini_pro_{}_output.txt\").format(languages[language])\n",
        "    with open(output_file_path, \"w\") as txt_file:\n",
        "        txt_file.write(extracted_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1lGP9y23y2K"
      },
      "source": [
        "**Gemini 1.5 Flash**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SpAq__743nE3"
      },
      "outputs": [],
      "source": [
        "num_docs = 11\n",
        "for doc_id in range(11, num_docs+1):\n",
        "    extracted_text = extract_text_gemini(genimi_gemini_flash, (\"/content/scan-font{}.pdf\").format(doc_id))\n",
        "    output_file_path = (\"/content/gemini_flash_font_output_{}.txt\").format(doc_id)\n",
        "    with open(output_file_path, \"w\") as txt_file:\n",
        "        txt_file.write(extracted_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JIY6uTML6Hru"
      },
      "outputs": [],
      "source": [
        "import vertexai\n",
        "\n",
        "from vertexai.generative_models import GenerativeModel, Part\n",
        "\n",
        "# TODO(developer): Update and un-comment below line\n",
        "# project_id = \"PROJECT_ID\"\n",
        "\n",
        "vertexai.init(project=940337059666, location=\"us-central1\")\n",
        "\n",
        "model = GenerativeModel(model_name=\"gemini-1.0-pro-vision-001\")\n",
        "\n",
        "response = model.generate_content(\n",
        "    [\n",
        "        Part.from_uri(\n",
        "            \"gs://cloud-samples-data/generative-ai/image/scones.jpg\",\n",
        "            mime_type=\"image/jpeg\",\n",
        "        ),\n",
        "        \"What is shown in this image?\",\n",
        "    ]\n",
        ")\n",
        "\n",
        "print(response.text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "czybTmwiKSRW"
      },
      "source": [
        "# **GPT 4**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C3x8DAM0KaWr"
      },
      "outputs": [],
      "source": [
        "!pip install openai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "02MxqPcgznWt"
      },
      "outputs": [],
      "source": [
        "import openai"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f1Rfi-5czSG8"
      },
      "outputs": [],
      "source": [
        "openai.api_key = 'sk-proj-YOl2xepEsNppWm3xLshlT3BlbkFJL04qQgahGxFcFGEClnQK'\n",
        "image_media_type = \"image/png\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "szlmMiN9ztl4"
      },
      "outputs": [],
      "source": [
        "def extract_text_gpt(model, pdf_path):\n",
        "    images = convert_pdf_to_images(pdf_path)\n",
        "    extracted_text = \"\"\n",
        "    headers = {\n",
        "        \"Content-Type\": \"application/json\",\n",
        "        \"Authorization\": f\"Bearer {openai.api_key}\"\n",
        "    }\n",
        "\n",
        "    for image in images:\n",
        "        base64_string = encode_image_to_base64(image)\n",
        "        payload = {\n",
        "            \"model\": model,\n",
        "            \"messages\": [\n",
        "              {\n",
        "                \"role\": \"user\",\n",
        "                \"content\": [\n",
        "                  {\n",
        "                    \"type\": \"text\",\n",
        "                    \"text\": \"Extract bounding boxes of all the tables present in this image. Return bounding boxes as liat of lists and don't provide any other text in the response.\"\n",
        "                  },\n",
        "                  {\n",
        "                    \"type\": \"image_url\",\n",
        "                    \"image_url\": {\n",
        "                      \"url\": f\"data:image/jpeg;base64,{base64_string}\"\n",
        "                    }\n",
        "                  }\n",
        "                ]\n",
        "              }\n",
        "            ],\n",
        "          }\n",
        "\n",
        "        response = requests.post(\"https://api.openai.com/v1/chat/completions\", headers=headers, json=payload)\n",
        "        response_json = response.json()\n",
        "\n",
        "        if \"choices\" in response_json and len(response_json[\"choices\"]) > 0:\n",
        "            extracted_text += response_json[\"choices\"][0][\"message\"][\"content\"]\n",
        "\n",
        "    return extracted_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "24_FkL6_flsn",
        "outputId": "7a4c2ca4-14bc-40f6-b47c-98f311e6808f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'In the image you provided, there isn\\'t a traditional visual \"table\" with borders, but there is a list that resembles a table structure, with items and corresponding page numbers. I\\'ll provide the bounding box for this section as it contains structured data which could be interpreted similar to a table layout.\\n\\nHere is the bounding box for the textual data that looks like a columnar listing (which may serve as \"table\" in loose terms) present in the image:\\n\\n- **Top-left corner**: (x: 25, y: 470)\\n- **Bottom-right corner**: (x: 415, y: 670)\\n\\nThis bounding box outlines the area where the list of engine components and their corresponding sections in a manual are presented.The image contains two tables with the following bounding boxes:\\n\\n1. The first table, titled \"DIMENSIONS\", has the bounding box:\\n   - Top-left corner: (39, 100)\\n   - Bottom-right corner: (397, 366)\\n\\n2. The second table, titled \"ENGINE SPECIFICATION\", is located:\\n   - Top-left corner: (39, 386)\\n   - Bottom-right corner: (397, 599)\\n\\nThese coordinates are approximate and measured in pixels from the top-left corner of the image which is assumed to be (0,0).The image contains a single large table. The bounding box for the table can be approximately described as covering most of the page, with a little margin on all sides. Here is a rough estimation of the bounding box coordinates based on the pixel dimensions given (assuming the total image size is the standard document size, typically at 437 pixels width and 613 pixels height):\\n\\n- **Left:** 30 pixels\\n- **Top:** 70 pixels\\n- **Right:** 407 pixels\\n- **Bottom:** 590 pixels\\n\\nThis encompasses the whole table from the headers \"Light Bulb\", \"Bulb type\", and \"Wattage\" down to the notes at the bottom of the table.The image contains one primary table positioned approximately at the top portion of the image. The bounding box for this table can be described as follows:\\n\\n- Left edge around column 35 pixels\\n- Top edge around row 140 pixels\\n- Right edge around column 403 pixels\\n- Bottom edge around row 385 pixels\\n\\nThese dimensions provide a general bounding box for the visible table in the image.The image contains a single table. Here are the bounding box coordinates for the table:\\n\\n- Top-left corner: (32, 140)\\n- Top-right corner: (408, 140)\\n- Bottom-left corner: (32, 374)\\n- Bottom-right corner: (408, 374)\\n\\nThese coordinates are in pixels and approximate, based on the visible boundaries of the table in the image.The image contains two tables. Here are their bounding boxes described in a format (x, y, width, height) based on a coordinate system with the top-left corner of the image as the origin (0, 0):\\n\\n1. The first table \"Temperature Range for SAE Viscosity Numbers\":\\n   - x: 62\\n   - y: 433\\n   - width: 315\\n   - height: 120\\n\\n2. The second table \"Petrol Engine Oil\":\\n   - x: 92\\n   - y: 580\\n   - width: 255\\n   - height: 45\\n\\nThese coordinates and dimensions are approximations that match the locations of tables as visible in the image provided.The image you provided doesn\\'t contain any tables. It features a dual column layout with text and images explaining how to locate the vehicle identification number (VIN) and vehicle certification label for a car. Each column includes an image of a car interior and a descriptive text underneath. There are no visible tables to extract bounding boxes from in this image.'"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "extracted_text = extract_text_gpt(\"gpt-4-turbo\", \"/content/hyundai_exter-24-30.pdf\")\n",
        "extracted_text"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slcGEKRq1kwh"
      },
      "source": [
        "**GPT 4 Turbo**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "24KZ_nZN0DyO"
      },
      "outputs": [],
      "source": [
        "for language in range(len(languages)):\n",
        "    pdf_path = f\"/content/{languages[language]}.pdf\"\n",
        "    extracted_text = extract_text_gpt(\"gpt-4-turbo\", pdf_path)\n",
        "    output_file_path = f\"/content/gpt4_turbo_{languages[language]}_output.txt\"\n",
        "    with open(output_file_path, \"w\") as txt_file:\n",
        "        txt_file.write(extracted_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ZBQwUd61znH"
      },
      "source": [
        "**GPT 4o**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AmMdPEU51Wtn"
      },
      "outputs": [],
      "source": [
        "for language in range(len(languages)):\n",
        "    pdf_path = f\"/content/{languages[language]}.pdf\"\n",
        "    extracted_text = extract_text_gpt(\"gpt-4o\", pdf_path)\n",
        "    output_file_path = f\"/content/gpt4o_{languages[language]}_output.txt\"\n",
        "    with open(output_file_path, \"w\") as txt_file:\n",
        "        txt_file.write(extracted_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MN8aesQO4rFx"
      },
      "source": [
        "# **Google Vision**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WZr-dP4N5zdl"
      },
      "outputs": [],
      "source": [
        "!pip install google-cloud-vision\n",
        "!pip install pdf2image"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dt7RZATJ4ILK",
        "collapsed": true
      },
      "outputs": [],
      "source": [
        "!sudo apt-get update\n",
        "!apt-get install poppler-utils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "Vd78XcqKElbz"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"] = \"/content/ai-drive-test-vision-ocr.json\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "jePfJZnC3ZVf"
      },
      "outputs": [],
      "source": [
        "from pdf2image import convert_from_path\n",
        "import base64\n",
        "from io import BytesIO\n",
        "from PIL import Image\n",
        "from google.cloud import vision\n",
        "\n",
        "def pdf_to_images(pdf_path):\n",
        "    images = convert_from_path(pdf_path)\n",
        "    image_paths = []\n",
        "    for i, image in enumerate(images):\n",
        "        image_path = f\"/tmp/page_{i}.png\"\n",
        "        image.save(image_path, \"PNG\")\n",
        "        image_paths.append(image_path)\n",
        "    return image_paths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "snf2koZ16oYU"
      },
      "outputs": [],
      "source": [
        "def extract_text_vision(path):\n",
        "    client = vision.ImageAnnotatorClient()\n",
        "\n",
        "    with open(path, \"rb\") as image_file:\n",
        "        content = image_file.read()\n",
        "\n",
        "    image = vision.Image(content=content)\n",
        "    response = client.document_text_detection(image=image)\n",
        "\n",
        "    extracted_text = \"\"\n",
        "    for page in response.full_text_annotation.pages:\n",
        "        for block in page.blocks:\n",
        "            for paragraph in block.paragraphs:\n",
        "                for word in paragraph.words:\n",
        "                    word_text = \"\".join([symbol.text for symbol in word.symbols])\n",
        "                    extracted_text += word_text + \" \"\n",
        "        extracted_text += \"\\n\"\n",
        "\n",
        "    return extracted_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "4JsTrKvHEfoy"
      },
      "outputs": [],
      "source": [
        "def detect_documents_vision(pdf_path):\n",
        "    pages = pdf_to_images(pdf_path)\n",
        "    extracted_text = \"\"\n",
        "    for pg in pages:\n",
        "        extracted_text += extract_text_vision(pg)\n",
        "    return extracted_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "IgVJNXqf7n-D"
      },
      "outputs": [],
      "source": [
        "for doc_id in range(1, 2):\n",
        "    pdf_path = \"/content/japanese2.pdf\"\n",
        "    extracted_text = detect_documents_vision(pdf_path)\n",
        "    output_file_path = f\"/content/vision_japanese_output.txt\"\n",
        "    with open(output_file_path, \"w\") as txt_file:\n",
        "        txt_file.write(extracted_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Florence**-2-large"
      ],
      "metadata": {
        "id": "tedrUKKhBH-6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install einops flash_attn timm"
      ],
      "metadata": {
        "id": "B4noKtMMBpaD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from PIL import Image, ImageDraw\n",
        "from IPython.display import display\n",
        "\n",
        "def draw_boxes(image_path, boxes):\n",
        "    image = Image.open(image_path).convert(\"RGB\")\n",
        "    draw = ImageDraw.Draw(image)\n",
        "\n",
        "    for box in boxes:\n",
        "        draw.rectangle(box, outline=\"red\", width=3)\n",
        "    display(image)"
      ],
      "metadata": {
        "id": "14KPiTfyUpMi"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import fitz\n",
        "import requests\n",
        "from PIL import Image\n",
        "from transformers import AutoProcessor, AutoModelForCausalLM"
      ],
      "metadata": {
        "id": "ve5DeQuwBPbg"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/Florence-2-large\", trust_remote_code=True)\n",
        "processor = AutoProcessor.from_pretrained(\"microsoft/Florence-2-large\", trust_remote_code=True)"
      ],
      "metadata": {
        "id": "MaXP9YRMBhXl"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = AutoModelForCausalLM.from_pretrained(\"microsoft/Florence-2-base\", trust_remote_code=True)\n",
        "processor = AutoProcessor.from_pretrained(\"microsoft/Florence-2-base\", trust_remote_code=True)"
      ],
      "metadata": {
        "id": "1rcc-e5AGn_n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"Extract bounding boxes of all the tables present in this page\""
      ],
      "metadata": {
        "id": "xwgEmKdvBknf"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_text_florence(pdf_path):\n",
        "    images = convert_from_path(pdf_path)\n",
        "    extracted_text = \"\"\n",
        "    # for image in images:\n",
        "    image = images[1]\n",
        "    inputs = processor(text=prompt, images=image, return_tensors=\"pt\")\n",
        "\n",
        "    generated_ids = model.generate(\n",
        "        input_ids=inputs[\"input_ids\"],\n",
        "        pixel_values=inputs[\"pixel_values\"],\n",
        "        max_new_tokens=1024,\n",
        "        num_beams=3,\n",
        "        do_sample=False\n",
        "    )\n",
        "    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]\n",
        "\n",
        "    parsed_answer = processor.post_process_generation(generated_text, task=\"<OD>\", image_size=(image.width, image.height))\n",
        "\n",
        "    print(parsed_answer)\n",
        "    return parsed_answer\n"
      ],
      "metadata": {
        "id": "bB-majwGBHAd"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "extracted_data = extract_text_florence(\"/content/table-data.pdf\")\n",
        "bbox = extracted_data['<OD>']['bboxes']\n",
        "bbox"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mch5i9PfDpu1",
        "outputId": "0be21a77-ea1b-4e8c-a332-47c19694f0eb"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'<OD>': {'bboxes': [[141.4739990234375, 219.3100128173828, 1173.7821044921875, 599.6900024414062], [0.6460000276565552, 0.9100000262260437, 1290.06201171875, 1817.27001953125]], 'labels': ['bounding boxes of all the tables present', 'this page']}}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[141.4739990234375, 219.3100128173828, 1173.7821044921875, 599.6900024414062],\n",
              " [0.6460000276565552, 0.9100000262260437, 1290.06201171875, 1817.27001953125]]"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "draw_boxes(\"/content/table-2.png\", bbox)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VLcTEUukUqZN",
        "outputId": "2b61feaa-e134-4b77-967d-10104ed57ab2"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=930x1310>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import DetrImageProcessor, DetrForObjectDetection\n",
        "from PIL import Image\n",
        "import requests\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as patches"
      ],
      "metadata": {
        "id": "g-anGRt3X6wu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processor = DetrImageProcessor.from_pretrained(\"microsoft/Florence-2-large\")\n",
        "model = DetrForObjectDetection.from_pretrained(\"microsoft/Florence-2-large\")"
      ],
      "metadata": {
        "id": "jMgIhWFxX9IW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "# Load the processor and model\n",
        "\n",
        "\n",
        "# Load the image\n",
        "images = convert_from_path(\"/content/table-data.pdf\")\n",
        "image = images[1]\n",
        "\n",
        "# Preprocess the image\n",
        "inputs = processor(images=image, return_tensors=\"pt\")\n",
        "\n",
        "# Perform object detection\n",
        "outputs = model(**inputs)\n",
        "\n",
        "# Extract boxes and labels\n",
        "target_sizes = torch.tensor([image.size[::-1]])\n",
        "results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)[0]\n",
        "\n",
        "# Filter boxes for tables (assuming label 74 is for 'table')\n",
        "table_boxes = [box for box, score, label in zip(results[\"boxes\"], results[\"scores\"], results[\"labels\"]) if label == 74 and score > 0.9]\n",
        "\n",
        "# Draw bounding boxes on the image\n",
        "fig, ax = plt.subplots(1, figsize=(16, 16))\n",
        "ax.imshow(image)\n",
        "\n",
        "for box in table_boxes:\n",
        "    xmin, ymin, xmax, ymax = box\n",
        "    rect = patches.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin, linewidth=2, edgecolor='r', facecolor='red')\n",
        "    ax.add_patch(rect)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "vy8IeDcWXrrC",
        "outputId": "338de2cc-631f-4dcc-d7ef-7e38ce3a99c3"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rcTaNw3f9k3Q"
      },
      "source": [
        "## **MiniCPM-Llama3-V-2_5**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HdqDTnIHFWyZ",
        "outputId": "4d90a19e-3f28-4989-deb5-2493e00d894d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'MiniCPM-V'...\n",
            "remote: Enumerating objects: 1028, done.\u001b[K\n",
            "remote: Counting objects: 100% (492/492), done.\u001b[K\n",
            "remote: Compressing objects: 100% (241/241), done.\u001b[K\n",
            "remote: Total 1028 (delta 389), reused 275 (delta 251), pack-reused 536\u001b[K\n",
            "Receiving objects: 100% (1028/1028), 189.53 MiB | 22.70 MiB/s, done.\n",
            "Resolving deltas: 100% (584/584), done.\n",
            "Updating files: 100% (134/134), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/OpenBMB/MiniCPM-V.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "90fQcg5VGQ8J",
        "outputId": "e0f888bf-d687-4999-ed88-9dd279036ce1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/MiniCPM-V\n"
          ]
        }
      ],
      "source": [
        "%cd /content/MiniCPM-V"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "38T-TEZ8FXlb"
      },
      "outputs": [],
      "source": [
        "!pip install -r /content/MiniCPM-V/requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8FGlbTPJ9soZ"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from PIL import Image\n",
        "from transformers import AutoModel, AutoTokenizer\n",
        "from chat import MiniCPMVChat, img2base64"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "id": "pKPLLy3hJC_K",
        "outputId": "79b4d794-f47f-43fd-bac4-6a1ac23f1df4"
      },
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "module 'torch._functorch.eager_transforms' has no attribute 'grad_and_value'",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-d4307ef60258>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mchat\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mMiniCPMVChat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mimg2base64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mjson\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmanual_seed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/content/MiniCPM-V/chat.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbase64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mio\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0maccelerate\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_checkpoint_and_dispatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_empty_weights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtransformers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAutoTokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAutoModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0m__version__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"0.30.1\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAccelerator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m from .big_modeling import (\n\u001b[1;32m     18\u001b[0m     \u001b[0mcpu_offload\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/accelerator.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhooks\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mhooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mcheckpointing\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_accelerator_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mload_custom_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_accelerator_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_custom_state\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mdata_loader\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDataLoaderDispatcher\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprepare_data_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskip_first_batches\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mhooks\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAlignDevicesHook\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/checkpointing.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mamp\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mGradScaler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m from .utils import (\n\u001b[0m\u001b[1;32m     25\u001b[0m     \u001b[0mMODEL_NAME\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0mOPTIMIZER_NAME\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mbnb\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mhas_4bit_bnb_layers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mload_and_quantize_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 182\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mfsdp_utils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_fsdp_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mload_fsdp_optimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_fsdp_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msave_fsdp_optimizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    183\u001b[0m from .launch import (\n\u001b[1;32m    184\u001b[0m     \u001b[0mPrepareForLaunch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/utils/fsdp_utils.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_torch_version\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\">=\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFSDP_PYTORCH_VERSION\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_torch_distributed_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheckpoint\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdist_cp\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdefault_planner\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDefaultLoadPlanner\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDefaultSavePlanner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheckpoint\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_sharded_optimizer_state_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/distributed/checkpoint/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mMetadata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m )\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mstate_dict_loader\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_state_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mstate_dict_saver\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0msave_state_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStorageReader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStorageWriter\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/distributed/checkpoint/state_dict_loader.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      8\u001b[0m )\n\u001b[1;32m      9\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mplanner\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLoadPlanner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mdefault_planner\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDefaultLoadPlanner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_DistWrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/distributed/checkpoint/default_planner.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_shard\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_utils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnarrow_tensor_by_index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tensor\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDTensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/distributed/_tensor/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    317\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    318\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_running_with_deploy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 319\u001b[0;31m     \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dynamo_utils\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/distributed/_tensor/_dynamo_utils.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dynamo\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mallow_in_graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistributed\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDTensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# dynamo/torch.compile utils for\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mallow_in_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_dynamo/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mallowed_functions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_frame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_frame\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresume_execution\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mbackends\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregistry\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlist_backends\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mregister_backend\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0mconvert_frame\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mreplay\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m from .decorators import (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_dynamo/allowed_functions.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_functorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeprecated\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdeprecated_func\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_symbolic_trace\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mis_fx_tracing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_functorch/deprecated.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[0msetup_docs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmap\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'torch.vmap'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[0msetup_docs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrad\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 113\u001b[0;31m \u001b[0msetup_docs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrad_and_value\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    114\u001b[0m \u001b[0msetup_docs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvjp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[0msetup_docs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjvp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_functorch/deprecated.py\u001b[0m in \u001b[0;36msetup_docs\u001b[0;34m(functorch_api, torch_func_api, new_api_name)\u001b[0m\n\u001b[1;32m     42\u001b[0m     \u001b[0mapi_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunctorch_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtorch_func_api\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m         \u001b[0mtorch_func_api\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_impl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapi_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m     \u001b[0;31m# See https://docs.python.org/3/using/cmdline.html#cmdoption-OO\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mtorch_func_api\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'torch._functorch.eager_transforms' has no attribute 'grad_and_value'"
          ]
        }
      ],
      "source": [
        "from chat import MiniCPMVChat, img2base64\n",
        "import torch\n",
        "import json\n",
        "\n",
        "torch.manual_seed(0)\n",
        "\n",
        "chat_model = MiniCPMVChat('openbmb/MiniCPM-Llama3-V-2_5')\n",
        "\n",
        "im_64 = img2base64('./assets/airplane.jpeg')\n",
        "\n",
        "# First round chat\n",
        "msgs = [{\"role\": \"user\", \"content\": \"Tell me the model of this aircraft.\"}]\n",
        "\n",
        "inputs = {\"image\": im_64, \"question\": json.dumps(msgs)}\n",
        "answer = chat_model.chat(inputs)\n",
        "print(answer)\n",
        "\n",
        "# Second round chat\n",
        "# pass history context of multi-turn conversation\n",
        "msgs.append({\"role\": \"assistant\", \"content\": answer})\n",
        "msgs.append({\"role\": \"user\", \"content\": \"Introduce something about Airbus A380.\"})\n",
        "\n",
        "inputs = {\"image\": im_64, \"question\": json.dumps(msgs)}\n",
        "answer = chat_model.chat(inputs)\n",
        "print(answer)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3cueFr9U9xaC"
      },
      "outputs": [],
      "source": [
        "model = AutoModel.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True, torch_dtype=torch.float16)\n",
        "model = model.to(device='cuda')\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained('openbmb/MiniCPM-Llama3-V-2_5', trust_remote_code=True)\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FR0rLo8M9jwq"
      },
      "outputs": [],
      "source": [
        "def extract_text_llama3():\n",
        "    image = Image.open('xx.jpg').convert('RGB')\n",
        "    question = 'What is in the image?'\n",
        "    msgs = [{'role': 'user', 'content': question}]\n",
        "\n",
        "    res = model.chat(\n",
        "        image=image,\n",
        "        msgs=msgs,\n",
        "        tokenizer=tokenizer,\n",
        "        sampling=True, # if sampling=False, beam_search will be used by default\n",
        "        temperature=0.7,\n",
        "        # system_prompt='' # pass system_prompt if needed\n",
        "    )\n",
        "    print(res)\n",
        "\n",
        "    generated_text = \"\"\n",
        "    for new_text in res:\n",
        "        generated_text += new_text\n",
        "        print(new_text, flush=True, end='')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wtSnC0LOAk2d"
      },
      "outputs": [],
      "source": [
        "# num_docs = 9\n",
        "for doc_id in range(1, 12):\n",
        "    pdf_path = f\"/content/scan-font{doc_id}.pdf\"\n",
        "    extracted_text = detect_documents_vision(pdf_path)\n",
        "    output_file_path = f\"/content/llama3_font_output_{doc_id}.txt\"\n",
        "    with open(output_file_path, \"w\") as txt_file:\n",
        "        txt_file.write(extracted_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yx8Ha5QpUaJs"
      },
      "source": [
        "# **Tesseract**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "70ZC9iAdJpNN"
      },
      "outputs": [],
      "source": [
        "!pip install pytesseract\n",
        "!pip install pdfminer\n",
        "!pip install pdf2image\n",
        "!pip install pdfminer.six"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oldQJ-GxME11"
      },
      "outputs": [],
      "source": [
        "!sudo apt-get update\n",
        "!sudo apt-get install libleptonica-dev tesseract-ocr tesseract-ocr-dev libtesseract-dev python3-pil tesseract-ocr-eng tesseract-ocr-script-latn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XU-l6lYAKuFr"
      },
      "outputs": [],
      "source": [
        "import fitz  # PyMuPDF\n",
        "from PIL import Image\n",
        "import pytesseract\n",
        "import io"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oq_2evyNJnwp"
      },
      "outputs": [],
      "source": [
        "\n",
        "\n",
        "# Define the path to your Tesseract-OCR executable\n",
        "# pytesseract.pytesseract.tesseract_cmd = r'/content/tesseract-ocr-w64-setup-5.3.4.20240503.exe'  # Update this path\n",
        "\n",
        "def extract_text_tesseract(pdf_path):\n",
        "    # Open the PDF file\n",
        "    pdf_document = fitz.open(pdf_path)\n",
        "    text = \"\"\n",
        "\n",
        "\n",
        "    for page_num in range(len(pdf_document)):\n",
        "        page = pdf_document.load_page(page_num)\n",
        "        pix = page.get_pixmap()\n",
        "\n",
        "        # Convert the Pixmap object to an image\n",
        "        img = PIL.Image.frombytes(\"RGB\", [pix.width, pix.height], pix.samples)\n",
        "\n",
        "        # Use PIL to open the image\n",
        "        # img = Image.open(io.BytesIO(img_data))\n",
        "        text += pytesseract.image_to_string(img) + \"\\n\"\n",
        "\n",
        "    return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tn2msVBHJ6kJ"
      },
      "outputs": [],
      "source": [
        "pdf_path = \"/content/scan-font11.pdf\"\n",
        "extracted_text = extract_text_tesseract(pdf_path)\n",
        "print(extracted_text)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "rcTaNw3f9k3Q"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}