Initial Commit

.github/workflows/publish.yml

@@ -0,0 +1,29 @@
+name: Python package
+on:
+  push:
+    tags:
+      - "v*.*.*"
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.11
+      - name: Install python dependencies
+        run: |
+          pip install poetry
+          poetry install
+          poetry remove torch
+          poetry run pip install torch --index-url https://download.pytorch.org/whl/cpu
+      - name: Build package
+        run: |
+          poetry build
+      - name: Publish package
+        env:
+          PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }}
+        run: |
+          poetry config pypi-token.pypi "$PYPI_TOKEN"
+          poetry publish

.github/workflows/tests.yml

@@ -0,0 +1,34 @@
+name: Integration test
+
+on: [push]
+
+env:
+  TORCH_DEVICE: "cpu"
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python 3.11
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.11
+      - name: Install python dependencies
+        run: |
+          pip install poetry
+          poetry install
+          poetry remove torch
+          poetry run pip install torch --index-url https://download.pytorch.org/whl/cpu
+      - name: Download benchmark data
+        run: |
+          wget -O benchmark_data.zip "https://drive.google.com/uc?export=download&id=1dbY0kBq2SUa885gmbLPUWSRzy5K7O5XJ"
+          unzip benchmark_data.zip
+          mv bench_data.json data/bench_data.json
+      - name: Run benchmark test
+        run: |
+          poetry run texify_benchmark --max 16
+          poetry run python scripts/verify_benchmark_scores.py data/bench_results.json
+        
+          
+

.gitignore

@@ -0,0 +1,166 @@
+private.py
+.DS_Store
+local.env
+experiments
+test_data
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+.idea/

LICENSE

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
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+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
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+
+    Texify latex image ocr
+    Copyright (C) 2023  Vikas Paruchuri
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    Texify  Copyright (C) 2023  Vikas Paruchuri
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -0,0 +1,175 @@
+# Texify
+
+Texify is an OCR model that converts images or pdfs containing math into markdown and LaTeX that can be rendered by MathJax ($$ and $ are delimiters).  It can run on CPU, GPU, or MPS.
+
+https://github.com/VikParuchuri/texify/assets/913340/882022a6-020d-4796-af02-67cb77bc084c
+
+Texify can work with block equations, or equations mixed with text (inline).  It will convert both the equations and the text.
+
+The closest open source comparisons to texify are [pix2tex](https://github.com/lukas-blecher/LaTeX-OCR) and [nougat](https://github.com/facebookresearch/nougat), although they're designed for different purposes:
+
+- Pix2tex is designed only for block LaTeX equations, and hallucinates more on text.
+- Nougat is designed to OCR entire pages, and hallucinates more on small images only containing math.
+
+Pix2tex is trained on im2latex, and nougat is trained on arxiv.  Texify is trained on a more diverse set of web data, and works on a range of images.
+
+See more details in the [benchmarks](#benchmarks) section.
+
+## Community
+
+[Discord](https://discord.gg//KuZwXNGnfH) is where we discuss future development.
+
+## Examples
+
+**Note** I added spaces after _ symbols because [Github math formatting is broken](https://github.com/github/markup/issues/1575).
+
+![Example 0](data/examples/0.png)
+
+**Detected Text** The potential $V_{i}$ of cell $\mathcal{C}_ {j}$ centred at position $\mathbf{r}_ {i}$ is related to the surface charge densities $\sigma_ {j}$ of cells $\mathcal{E}_ {j}$ $j\in[1,N]$ through the superposition principle as:
+
+$$V_ {i}\,=\,\sum_ {j=0}^{N}\,\frac{\sigma_ {j}}{4\pi\varepsilon_ {0}}\,\int_{\mathcal{E}_ {j}}\frac{1}{\left|\mathbf{r}_ {i}-\mathbf{r}^{\prime}\right|}\,\mathrm{d}^{2}\mathbf{r}^{\prime}\,=\,\sum_{j=0}^{N}\,Q_ {ij}\,\sigma_{j},$$
+
+where the integral over the surface of cell $\mathcal{C}_ {j}$ only depends on $\mathcal{C}{j}$ shape and on the relative position of the target point $\mathbf{r}_ {i}$ with respect to $\mathcal{C}_ {j}$ location, as $\sigma_ {j}$ is assumed constant over the whole surface of cell $\mathcal{C}_ {j}$.
+
+| Image                      | OCR Markdown              |
+|----------------------------|---------------------------|
+| [1](data/examples/100.png) | [1](data/examples/100.md) |
+| [2](data/examples/300.png) | [2](data/examples/300.md) |
+| [3](data/examples/400.png) | [3](data/examples/400.md) |
+
+# Installation
+
+You'll need python 3.10+ and PyTorch. You may need to install the CPU version of torch first if you're not using a Mac or a GPU machine.  See [here](https://pytorch.org/get-started/locally/) for more details.
+
+Install with:
+
+```
+`pip install texify`
+```
+
+Model weights will automatically download the first time you run it.
+
+# Usage
+
+- Inspect the settings in `texify/settings.py`.  You can override any settings with environment variables.
+- Your torch device will be automatically detected, but you can override this.  For example, `TORCH_DEVICE=cuda` or `TORCH_DEVICE=mps`.
+
+## Usage tips
+
+- Don't make your boxes too small or too large.  See the examples and the video above for good crops.
+- Texify is sensitive to how you draw the box around the text you want to OCR. If you get bad results, try selecting a slightly different box, or splitting the box into 2+.  You can also try changing the `TEMPERATURE` setting.
+- Sometimes, KaTeX won't be able to render an equation (red error), but it will still be valid LaTeX.  You can copy the LaTeX and render it elsewhere.
+
+## App for interactive conversion
+
+I've included a streamlit app that lets you interactively select and convert equations from images or PDF files.  Run it with:
+
+```
+texify_gui
+```
+
+The app will allow you to select the specific equations you want to convert on each page, then render the results with KaTeX and enable easy copying.
+
+## Convert images
+
+You can OCR a single image or a folder of images with:
+
+```
+texify /path/to/folder_or_file --max 8 --json_path results.json
+```
+
+- `--max` is how many images in the folder to convert at most.  Omit this to convert all images in the folder.
+- `--json_path` is an optional path to a json file where the results will be saved.  If you omit this, the results will be saved to `data/results.json`.
+
+## Import and run
+
+You can import texify and run it in python code:
+
+```
+from texify.inference import batch_inference
+from texify.model.model import load_model
+from texify.model.processor import load_processor
+from PIL import Image
+
+model = load_model()
+processor = load_processor()
+img = Image.open("test.png") # Your image name here
+results = batch_inference([img], model, processor)
+```
+
+# Manual install
+
+If you want to develop texify, you can install it manually:
+
+- `git clone https://github.com/VikParuchuri/texify.git`
+- `cd texify`
+- `poetry install` # Installs main and dev dependencies
+
+# Limitations
+
+OCR is complicated, and texify is not perfect.  Here are some known limitations:
+
+- The OCR is dependent on how you crop the image.  If you get bad results, try a different selection/crop.  Or try changing the `TEMPERATURE` setting.
+- Texify will OCR equations and surrounding text, but is not good for general purpose OCR.  Think sections of a page instead of a whole page.
+- Texify was mostly trained with 96 DPI images, and only at a max 420x420 resolution.  Very wide or very tall images may not work well.
+- It works best with English, although it should support other languages with similar character sets.
+- The output format will be markdown with embedded LaTeX for equations (close to Github flavored markdown).  It will not be pure LaTeX.
+
+# Benchmarks
+
+Benchmarking OCR quality is hard - you ideally need a parallel corpus that models haven't been trained on.  I sampled from arxiv and im2latex to create the benchmark set.
+
+![Benchmark results](data/images/texify_bench.png)
+
+Each model is trained on one of the benchmark tasks:
+
+- Nougat was trained on arxiv, possibly the images in the benchmark.
+- Pix2tex was trained on im2latex.
+- Texify was trained on im2latex. It was trained on arxiv, but not the images in the benchmark.
+
+Although this makes the benchmark results biased, it does seem like a good compromise, since nougat and pix2tex don't work as well out of domain.  Note that neither pix2tex or nougat is really designed for this task (OCR inline equations and text), so this is not a perfect comparison.
+
+| Model   | BLEU ⬆       | METEOR ⬆     | Edit Distance ⬇ |
+|---------|--------------|--------------|-----------------|
+| pix2tex | 0.382659     | 0.543363     | 0.352533        |
+| nougat  | 0.697667     | 0.668331     | 0.288159        |
+| texify  | **0.842349** | **0.885731** | **0.0651534**   |
+
+## Running your own benchmarks
+
+You can benchmark the performance of texify on your machine.  
+
+- Follow the manual install instructions above.
+- If you want to use pix2tex, run `pip install pix2tex`
+- If you want to use nougat, run `pip install nougat-ocr`
+- Download the benchmark data [here](https://drive.google.com/file/d/1dbY0kBq2SUa885gmbLPUWSRzy5K7O5XJ/view?usp=sharing) and put it in the `data` folder.
+- Run `benchmark.py` like this:
+
+```
+python benchmark.py --max 100 --pix2tex --nougat --data_path data/bench_data.json --result_path data/bench_results.json
+```
+
+This will benchmark marker against pix2tex and nougat.  It will do batch inference with texify and nougat, but not with pix2tex, since I couldn't find an option for batching.
+
+- `--max` is how many benchmark images to convert at most.
+- `--data_path` is the path to the benchmark data.  If you omit this, it will use the default path.
+- `--result_path` is the path to the benchmark results.  If you omit this, it will use the default path.
+- `--pix2tex` specifies whether to run pix2tex (Latex-OCR) or not.
+- `--nougat` specifies whether to run nougat or not.
+
+# Training
+
+Texify was trained on latex images and paired equations from across the web.  It includes the [im2latex](https://github.com/guillaumegenthial/im2latex) dataset.  Training happened on 4x A6000s for 2 days (~6 epochs).
+
+# Commercial usage
+
+This model is trained on top of the openly licensed [Donut](https://huggingface.co/naver-clova-ix/donut-base) model, and thus can be used for commercial purposes.  Model weights are licensed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license.
+
+# Thanks
+
+This work would not have been possible without lots of amazing open source work.  I particularly want to acknowledge [Lukas Blecher](https://github.com/lukas-blecher), whose work on Nougat and pix2tex was key for this project.  I learned a lot from his code, and used parts of it for texify.
+
+- [im2latex](https://github.com/guillaumegenthial/im2latex) - one of the datasets used for training
+- [Donut](https://huggingface.co/naver-clova-ix/donut-base) from Naver, the base model for texify
+- [Nougat](https://github.com/facebookresearch/nougat) - I used the tokenizer from Nougat
+- [Latex-OCR](https://github.com/lukas-blecher/LaTeX-OCR) - The original open source Latex OCR project

benchmark.py

@@ -0,0 +1,226 @@
+import argparse
+import os.path
+import random
+import time
+from functools import partial
+
+import evaluate
+from tabulate import tabulate
+from tqdm import tqdm
+
+from texify.inference import batch_inference
+from texify.model.model import load_model
+from texify.model.processor import load_processor
+from PIL import Image
+from texify.settings import settings
+import json
+import base64
+import io
+from rapidfuzz.distance import Levenshtein
+
+
+def normalize_text(text):
+    # Replace fences
+    text = text.replace("$", "")
+    text = text.replace("\[", "")
+    text = text.replace("\]", "")
+    text = text.replace("\(", "")
+    text = text.replace("\)", "")
+    text = text.strip()
+    return text
+
+
+def score_text(predictions, references):
+    bleu = evaluate.load("bleu")
+    bleu_results = bleu.compute(predictions=predictions, references=references)
+
+    meteor = evaluate.load('meteor')
+    meteor_results = meteor.compute(predictions=predictions, references=references)
+
+    lev_dist = []
+    for p, r in zip(predictions, references):
+        lev_dist.append(Levenshtein.normalized_distance(p, r))
+
+    return {
+        'bleu': bleu_results["bleu"],
+        'meteor': meteor_results['meteor'],
+        'edit': sum(lev_dist) / len(lev_dist)
+    }
+
+
+def image_to_pil(image):
+    decoded = base64.b64decode(image)
+    return Image.open(io.BytesIO(decoded))
+
+
+def load_images(source_data):
+    images = [sd["image"] for sd in source_data]
+    images = [image_to_pil(image) for image in images]
+    return images
+
+
+def inference_texify(source_data, model, processor):
+    images = load_images(source_data)
+
+    write_data = []
+    for i in tqdm(range(0, len(images), settings.BATCH_SIZE), desc="Texify inference"):
+        batch = images[i:i+settings.BATCH_SIZE]
+        text = batch_inference(batch, model, processor)
+        for j, t in enumerate(text):
+            eq_idx = i + j
+            write_data.append({"text": t, "equation": source_data[eq_idx]["equation"]})
+
+    return write_data
+
+
+def inference_pix2tex(source_data):
+    from pix2tex.cli import LatexOCR
+    model = LatexOCR()
+
+    images = load_images(source_data)
+    write_data = []
+    for i in tqdm(range(len(images)), desc="Pix2tex inference"):
+        try:
+            text = model(images[i])
+        except ValueError:
+            # Happens when resize fails
+            text = ""
+        write_data.append({"text": text, "equation": source_data[i]["equation"]})
+
+    return write_data
+
+
+def image_to_bmp(image):
+    img_out = io.BytesIO()
+    image.save(img_out, format="BMP")
+    return img_out
+
+
+def inference_nougat(source_data, batch_size=1):
+    import torch
+    from nougat.postprocessing import markdown_compatible
+    from nougat.utils.checkpoint import get_checkpoint
+    from nougat.utils.dataset import ImageDataset
+    from nougat.utils.device import move_to_device
+    from nougat import NougatModel
+
+    # Load images, then convert to bmp format for nougat
+    images = load_images(source_data)
+    images = [image_to_bmp(image) for image in images]
+    predictions = []
+
+    ckpt = get_checkpoint(None, model_tag="0.1.0-small")
+    model = NougatModel.from_pretrained(ckpt)
+    if settings.TORCH_DEVICE_MODEL != "cpu":
+        move_to_device(model, bf16=settings.CUDA, cuda=settings.CUDA)
+    model.eval()
+
+    dataset = ImageDataset(
+        images,
+        partial(model.encoder.prepare_input, random_padding=False),
+    )
+
+    # Batch sizes higher than 1 explode memory usage on CPU/MPS
+    dataloader = torch.utils.data.DataLoader(
+        dataset,
+        batch_size=batch_size,
+        pin_memory=True,
+        shuffle=False,
+    )
+
+    for idx, sample in tqdm(enumerate(dataloader), desc="Nougat inference", total=len(dataloader)):
+        model.config.max_length = settings.MAX_TOKENS
+        model_output = model.inference(image_tensors=sample, early_stopping=False)
+        output = [markdown_compatible(o) for o in model_output["predictions"]]
+        predictions.extend(output)
+    return predictions
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Benchmark the performance of texify.")
+    parser.add_argument("--data_path", type=str, help="Path to JSON file with source images/equations", default=os.path.join(settings.DATA_DIR, "bench_data.json"))
+    parser.add_argument("--result_path", type=str, help="Path to JSON file to save results to.", default=os.path.join(settings.DATA_DIR, "bench_results.json"))
+    parser.add_argument("--max", type=int, help="Maximum number of images to benchmark.", default=None)
+    parser.add_argument("--pix2tex", action="store_true", help="Run pix2tex scoring", default=False)
+    parser.add_argument("--nougat", action="store_true", help="Run nougat scoring", default=False)
+    args = parser.parse_args()
+
+    source_path = os.path.abspath(args.data_path)
+    result_path = os.path.abspath(args.result_path)
+    os.makedirs(os.path.dirname(result_path), exist_ok=True)
+    model = load_model()
+    processor = load_processor()
+
+    with open(source_path, "r") as f:
+        source_data = json.load(f)
+
+    if args.max:
+        random.seed(1)
+        source_data = random.sample(source_data, args.max)
+
+    start = time.time()
+    predictions = inference_texify(source_data, model, processor)
+    times = {"texify": time.time() - start}
+    text = [normalize_text(p["text"]) for p in predictions]
+    references = [normalize_text(p["equation"]) for p in predictions]
+
+    scores = score_text(text, references)
+
+    write_data = {
+        "texify": {
+            "scores": scores,
+            "text": [{"prediction": p, "reference": r} for p, r in zip(text, references)]
+        }
+    }
+
+    if args.pix2tex:
+        start = time.time()
+        predictions = inference_pix2tex(source_data)
+        times["pix2tex"] = time.time() - start
+
+        p_text = [normalize_text(p["text"]) for p in predictions]
+
+        p_scores = score_text(p_text, references)
+
+        write_data["pix2tex"] = {
+            "scores": p_scores,
+            "text": [{"prediction": p, "reference": r} for p, r in zip(p_text, references)]
+        }
+
+    if args.nougat:
+        start = time.time()
+        predictions = inference_nougat(source_data)
+        times["nougat"] = time.time() - start
+        n_text = [normalize_text(p) for p in predictions]
+
+        n_scores = score_text(n_text, references)
+
+        write_data["nougat"] = {
+            "scores": n_scores,
+            "text": [{"prediction": p, "reference": r} for p, r in zip(n_text, references)]
+        }
+
+    score_table = []
+    score_headers = ["bleu", "meteor", "edit"]
+    score_dirs = ["⬆", "⬆", "⬇", "⬇"]
+
+    for method in write_data.keys():
+        score_table.append([method, *[write_data[method]["scores"][h] for h in score_headers], times[method]])
+
+    score_headers.append("time taken (s)")
+    score_headers = [f"{h} {d}" for h, d in zip(score_headers, score_dirs)]
+    print()
+    print(tabulate(score_table, headers=["Method", *score_headers]))
+    print()
+    print("Higher is better for BLEU and METEOR, lower is better for edit distance and time taken.")
+    print("Note that pix2tex is unbatched (I couldn't find a batch inference method in the docs), so time taken is higher than it should be.")
+
+    with open(result_path, "w") as f:
+        json.dump(write_data, f, indent=4)
+
+
+if __name__ == "__main__":
+    main()
+
+
+

data/.gitignore

@@ -0,0 +1,6 @@
+*
+!.gitignore
+!examples
+!examples/*
+!images
+!images/*

data/examples/0.md

@@ -0,0 +1,5 @@
+The potential $V_{i}$ of cell $\mathcal{C}_ {j}$ centred at position $\mathbf{r}_ {i}$ is related to the surface charge densities $\sigma_ {j}$ of cells $\mathcal{E}_ {j}$ $j\in[1,N]$ through the superposition principle as:
+
+$$V_ {i}\,=\,\sum_ {j=0}^{N}\,\frac{\sigma_ {j}}{4\pi\varepsilon_ {0}}\,\int_{\mathcal{E}_ {j}}\frac{1}{\left|\mathbf{r}_ {i}-\mathbf{r}^{\prime}\right|}\,\mathrm{d}^{2}\mathbf{r}^{\prime}\,=\,\sum_{j=0}^{N}\,Q_ {ij}\,\sigma_{j},$$
+
+where the integral over the surface of cell $\mathcal{C}_ {j}$ only depends on $\mathcal{C}{j}$ shape and on the relative position of the target point $\mathbf{r}_ {i}$ with respect to $\mathcal{C}_ {j}$ location, as $\sigma_ {j}$ is assumed constant over the whole surface of cell $\mathcal{C}_ {j}$.

data/examples/100.md

@@ -0,0 +1 @@
+Following , the minimal energy fraction the muon receives in the pion's rest frame is $r_ {\pi}=(m_ {\mu}/m_ {\pi})^2\approx0.57$, when it is emitted against the direction of movement, or 1 when it coincides with the pion's direction.

data/examples/300.md

@@ -0,0 +1,4 @@
+
+$$\mid\frac{1}{x}=\frac{1}{c}\mid=\mid\frac{c-x}{xc}\mid=\frac{1}{\left\vert x\right\vert}\cdot\frac{1}{\left\vert c\right\vert}\cdot\left\vert x-c\right\vert$$
+
+The factor $$\frac{1}{\left\vert x\right\vert}$$ is not good if its near 0. 

data/examples/400.md

@@ -0,0 +1,9 @@
+Then the results are that afterward:
+
+For every value of $\lambda$, there is a probability of $|\langle\Psi|\Psi_\lambda\rangle|^2$ that the system is in state $|\Psi_\lambda\rangle$
+
+This is captured by the density matrix formalism as the transition
+
+$|\Psi\rangle\langle\Psi|\Rightarrow\sum_\lambda|\langle\Psi|\Psi_\lambda\rangle|^2|\Psi_\lambda\rangle\langle\Psi_\lambda|$
+
+atyy I guess thinking about it classically, Demystifier's argument must be right. 

ocr_app.py

@@ -0,0 +1,167 @@
+import io
+
+import pandas as pd
+import streamlit as st
+from streamlit_drawable_canvas import st_canvas
+import hashlib
+import pypdfium2
+
+from texify.inference import batch_inference
+from texify.model.model import load_model
+from texify.model.processor import load_processor
+from texify.settings import settings
+import subprocess
+import re
+from PIL import Image
+
+MAX_WIDTH = 1000
+
+
+def replace_katex_invalid(string):
+    # KaTeX cannot render all LaTeX, so we need to replace some things
+    string = re.sub(r'\\tag\{.*?\}', '', string)
+    string = re.sub(r'\\Big\{(.*?)\}|\\big\{(.*?)\}', r'\1\2', string)
+    return string
+
+@st.cache_resource()
+def load_model_cached():
+    return load_model()
+
+
+@st.cache_resource()
+def load_processor_cached():
+    return load_processor()
+
+
+@st.cache_data()
+def infer_image(pil_image, bbox, temperature):
+    input_img = pil_image.crop(bbox)
+    model_output = batch_inference([input_img], model, processor, temperature=temperature)
+    return model_output[0]
+
+
+def open_pdf(pdf_file):
+    stream = io.BytesIO(pdf_file.getvalue())
+    return pypdfium2.PdfDocument(stream)
+
+
+@st.cache_data()
+def get_page_image(pdf_file, page_num, dpi=96):
+    doc = open_pdf(pdf_file)
+    renderer = doc.render(
+        pypdfium2.PdfBitmap.to_pil,
+        page_indices=[page_num - 1],
+        scale=dpi / 72,
+    )
+    png = list(renderer)[0]
+    png_image = png.convert("RGB")
+    return png_image
+
+
+@st.cache_data()
+def get_uploaded_image(in_file):
+    return Image.open(in_file).convert("RGB")
+
+
+@st.cache_data()
+def page_count(pdf_file):
+    doc = open_pdf(pdf_file)
+    return len(doc)
+
+
+def get_canvas_hash(pil_image):
+    return hashlib.md5(pil_image.tobytes()).hexdigest()
+
+
+@st.cache_data()
+def get_image_size(pil_image):
+    if pil_image is None:
+        return 800, 600
+    height, width = pil_image.height, pil_image.width
+    if width > MAX_WIDTH:
+        scale = MAX_WIDTH / width
+        height = int(height * scale)
+        width = MAX_WIDTH
+    return height, width
+
+
+st.set_page_config(layout="wide")
+
+top_message = """### Texify
+
+After the model loads, upload an image or a pdf, then draw a box around the equation or text you want to OCR by clicking and dragging. Texify will convert it to Markdown with LaTeX math on the right.
+
+If you have already cropped your image, select "OCR image" in the sidebar instead.
+"""
+
+st.markdown(top_message)
+col1, col2 = st.columns([.7, .3])
+
+model = load_model_cached()
+processor = load_processor_cached()
+
+in_file = st.sidebar.file_uploader("PDF file or image:", type=["pdf", "png", "jpg", "jpeg", "gif", "webp"])
+if in_file is None:
+    st.stop()
+
+filetype = in_file.type
+whole_image = False
+if "pdf" in filetype:
+    page_count = page_count(in_file)
+    page_number = st.sidebar.number_input(f"Page number out of {page_count}:", min_value=1, value=1, max_value=page_count)
+
+    pil_image = get_page_image(in_file, page_number)
+else:
+    pil_image = get_uploaded_image(in_file)
+    whole_image = st.sidebar.button("OCR image")
+
+temperature = st.sidebar.slider("Generation temperature:", min_value=0.0, max_value=1.0, value=0.0, step=0.05)
+
+canvas_hash = get_canvas_hash(pil_image) if pil_image else "canvas"
+
+with col1:
+    # Create a canvas component
+    canvas_result = st_canvas(
+        fill_color="rgba(255, 165, 0, 0.1)",  # Fixed fill color with some opacity
+        stroke_width=1,
+        stroke_color="#FFAA00",
+        background_color="#FFF",
+        background_image=pil_image,
+        update_streamlit=True,
+        height=get_image_size(pil_image)[0],
+        width=get_image_size(pil_image)[1],
+        drawing_mode="rect",
+        point_display_radius=0,
+        key=canvas_hash,
+    )
+
+if canvas_result.json_data is not None or whole_image:
+    objects = pd.json_normalize(canvas_result.json_data["objects"])  # need to convert obj to str because PyArrow
+    bbox_list = None
+    if objects.shape[0] > 0:
+        boxes = objects[objects["type"] == "rect"][["left", "top", "width", "height"]]
+        boxes["right"] = boxes["left"] + boxes["width"]
+        boxes["bottom"] = boxes["top"] + boxes["height"]
+        bbox_list = boxes[["left", "top", "right", "bottom"]].values.tolist()
+    if whole_image:
+        bbox_list = [(0, 0, pil_image.width, pil_image.height)]
+
+    if bbox_list:
+        with col2:
+            inferences = [infer_image(pil_image, bbox, temperature) for bbox in bbox_list]
+            for idx, inference in enumerate(reversed(inferences)):
+                st.markdown(f"### {len(inferences) - idx}")
+                katex_markdown = replace_katex_invalid(inference)
+                st.markdown(katex_markdown)
+                st.code(inference)
+                st.divider()
+
+with col2:
+    tips = """
+    ### Usage tips
+    - Don't make your boxes too small or too large.  See the examples and the video in the [README](https://github.com/vikParuchuri/texify) for more info.
+    - Texify is sensitive to how you draw the box around the text you want to OCR. If you get bad results, try selecting a slightly different box, or splitting the box into multiple.
+    - You can try changing the temperature value on the left if you don't get good results.  This controls how "creative" the model is.
+    - Sometimes KaTeX won't be able to render an equation (red error text), but it will still be valid LaTeX.  You can copy the LaTeX and render it elsewhere.
+    """
+    st.markdown(tips)

ocr_image.py

@@ -0,0 +1,67 @@
+import argparse
+import os.path
+
+from texify.inference import batch_inference
+from texify.model.model import load_model
+from texify.model.processor import load_processor
+from PIL import Image
+from texify.settings import settings
+from texify.util import is_valid_image
+import json
+
+
+def inference_single_image(image_path, json_path, model, processor):
+    image = Image.open(image_path)
+    text = batch_inference([image], model, processor)
+    write_data = [{"image_path": image_path, "text": text[0]}]
+    with open(json_path, "w+") as f:
+        json_repr = json.dumps(write_data, indent=4)
+        f.write(json_repr)
+
+
+def inference_image_dir(image_dir, json_path, model, processor, max=None):
+    image_paths = [os.path.join(image_dir, image_name) for image_name in os.listdir(image_dir)]
+    image_paths = [ip for ip in image_paths if is_valid_image(ip)]
+    if max:
+        image_paths = image_paths[:max]
+
+    write_data = []
+    for i in range(0, len(image_paths), settings.BATCH_SIZE):
+        batch = image_paths[i:i+settings.BATCH_SIZE]
+        images = [Image.open(image_path) for image_path in batch]
+        text = batch_inference(images, model, processor)
+        for image_path, t in zip(batch, text):
+            write_data.append({"image_path": image_path, "text": t})
+
+    with open(json_path, "w+") as f:
+        json_repr = json.dumps(write_data, indent=4)
+        f.write(json_repr)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="OCR an image of a LaTeX equation.")
+    parser.add_argument("image", type=str, help="Path to image or folder of images to OCR.")
+    parser.add_argument("--max", type=int, help="Maximum number of images to OCR if a folder is passes.", default=None)
+    parser.add_argument("--json_path", type=str, help="Path to JSON file to save results to.", default=os.path.join(settings.DATA_DIR, "results.json"))
+    args = parser.parse_args()
+
+    image_path = args.image
+    model = load_model()
+    processor = load_processor()
+
+    json_path = os.path.abspath(args.json_path)
+    os.makedirs(os.path.dirname(json_path), exist_ok=True)
+
+    if os.path.isfile(image_path):
+        inference_single_image(image_path, json_path, model, processor)
+    else:
+        inference_image_dir(image_path, json_path, model, processor, args.max)
+
+    print(f"Wrote results to {json_path}")
+
+
+if __name__ == "__main__":
+    main()
+
+
+

pyproject.toml

@@ -0,0 +1,47 @@
+[tool.poetry]
+name = "texify"
+version = "0.1.6"
+description = "OCR for latex images"
+authors = ["Vik Paruchuri <[email protected]>"]
+readme = "README.md"
+license = "GPL-3.0-or-later"
+repository = "https://github.com/VikParuchuri/texify"
+keywords = ["ocr", "latex", "markdown", "pdf"]
+include = [
+    "ocr_app.py",
+    "ocr_image.py",
+    "run_ocr_app.py",
+    "benchmark.py"
+]
+
+[tool.poetry.dependencies]
+python = ">=3.10,<4.0"
+streamlit = "^1.29.0"
+transformers = "^4.36.2"
+torch = "^2.1.2"
+pydantic = "^2.5.2"
+pydantic-settings = "^2.1.0"
+Pillow = "^10.1.0"
+numpy = "^1.26.2"
+pypdfium2 = "^4.25.0"
+python-dotenv = "^1.0.0"
+watchdog = "^3.0.0"
+ftfy = "^6.1.3"
+tabulate = "^0.9.0"
+streamlit-drawable-canvas-jsretry = "^0.9.3"
+
+[tool.poetry.group.dev.dependencies]
+jupyter = "^1.0.0"
+evaluate = "^0.4.1"
+rapidfuzz = "^3.5.2"
+pyperclip = "^1.8.2"
+nltk = "^3.8.1"
+
+[tool.poetry.scripts]
+texify = "ocr_image:main"
+texify_gui = "run_ocr_app:run_app"
+texify_benchmark = "benchmark:main"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

run_ocr_app.py

@@ -0,0 +1,8 @@
+import subprocess
+import os
+
+
+def run_app():
+    cur_dir = os.path.dirname(os.path.abspath(__file__))
+    ocr_app_path = os.path.join(cur_dir, "ocr_app.py")
+    subprocess.run(["streamlit", "run", ocr_app_path])

scripts/verify_benchmark_scores.py

@@ -0,0 +1,20 @@
+import json
+import argparse
+
+
+def verify_scores(file_path):
+    with open(file_path, 'r') as file:
+        data = json.load(file)
+
+    scores = data["texify"]["scores"]
+
+    if scores["bleu"] <= 0.6 or scores["meteor"] <= 0.6 or scores["edit"] > 0.2:
+        print(scores)
+        raise ValueError("Scores do not meet the required threshold")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Verify benchmark scores")
+    parser.add_argument("file_path", type=str, help="Path to the json file")
+    args = parser.parse_args()
+    verify_scores(args.file_path)