# What does this PR do?

[Sorry for the spams, GitHub had some issues]

Same as #24075, but that PR got freezed after I force pushed (after rebase), and my changes to address the comments were not able to appear)

(amy have already approved #24075)

----

Byebye!

EOL of python 3.7 is `2023/06/27`.

https://endoflife.date/python
⚠️ Time to say goodbye to py37. # What does this PR do?

[Sorry for the spams, GitHub had some issues]

Same as #24075, but that PR got freezed after I force pushed (after rebase), and my changes to address the comments were not able to appear)

(amy have already approved #24075)

----

Byebye!

EOL of python 3.7 is `2023/06/27`.

https://endoflife.date/python
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# MGP-STR

## Overview

The MGP-STR model was proposed in [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao. MGP-STR is a conceptually **simple** yet **powerful** vision Scene Text Recognition (STR) model, which is built upon the [Vision Transformer (ViT)](vit). To integrate linguistic knowledge, Multi-Granularity Prediction (MGP) strategy is proposed to inject information from the language modality into the model in an implicit way.

The abstract from the paper is the following:

*Scene text recognition (STR) has been an active research topic in computer vision for years. To tackle this challenging problem, numerous innovative methods have been successively proposed and incorporating linguistic knowledge into STR models has recently become a prominent trend. In this work, we first draw inspiration from the recent progress in Vision Transformer (ViT) to construct a conceptually simple yet powerful vision STR model, which is built upon ViT and outperforms previous state-of-the-art models for scene text recognition, including both pure vision models and language-augmented methods. To integrate linguistic knowledge, we further propose a Multi-Granularity Prediction strategy to inject information from the language modality into the model in an implicit way, i.e. , subword representations (BPE and WordPiece) widely-used in NLP are introduced into the output space, in addition to the conventional character level representation, while no independent language model (LM) is adopted. The resultant algorithm (termed MGP-STR) is able to push the performance envelop of STR to an even higher level. Specifically, it achieves an average recognition accuracy of 93.35% on standard benchmarks.*

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/mgp_str_architecture.png"
alt="drawing" width="600"/>

<small> MGP-STR architecture. Taken from the <a href="https://arxiv.org/abs/2209.03592">original paper</a>. </small>

Tips:

- MGP-STR is trained on two synthetic datasets [MJSynth]((http://www.robots.ox.ac.uk/~vgg/data/text/)) (MJ) and SynthText(http://www.robots.ox.ac.uk/~vgg/data/scenetext/) (ST) without fine-tuning on other datasets. It achieves state-of-the-art results on six standard Latin scene text benchmarks, including 3 regular text datasets (IC13, SVT, IIIT) and 3 irregular ones (IC15, SVTP, CUTE).
- This model was contributed by [yuekun](https://huggingface.co/yuekun). The original code can be found [here](https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/MGP-STR).

## Inference

[`MgpstrModel`] accepts images as input and generates three types of predictions, which represent textual information at different granularities.
The three types of predictions are fused to give the final prediction result.

The [`ViTImageProcessor`] class is responsible for preprocessing the input image and
[`MgpstrTokenizer`] decodes the generated character tokens to the target string. The
[`MgpstrProcessor`] wraps [`ViTImageProcessor`] and [`MgpstrTokenizer`]
into a single instance to both extract the input features and decode the predicted token ids.

- Step-by-step Optical Character Recognition (OCR)

``` py
>>> from transformers import MgpstrProcessor, MgpstrForSceneTextRecognition
>>> import requests
>>> from PIL import Image

>>> processor = MgpstrProcessor.from_pretrained('alibaba-damo/mgp-str-base')
>>> model = MgpstrForSceneTextRecognition.from_pretrained('alibaba-damo/mgp-str-base')

>>> # load image from the IIIT-5k dataset
>>> url = "https://i.postimg.cc/ZKwLg2Gw/367-14.png"
>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

>>> pixel_values = processor(images=image, return_tensors="pt").pixel_values
>>> outputs = model(pixel_values)

>>> generated_text = processor.batch_decode(outputs.logits)['generated_text']
```

## MgpstrConfig

[[autodoc]] MgpstrConfig

## MgpstrTokenizer

[[autodoc]] MgpstrTokenizer
    - save_vocabulary

## MgpstrProcessor

[[autodoc]] MgpstrProcessor
    - __call__
    - batch_decode

## MgpstrModel

[[autodoc]] MgpstrModel
    - forward

## MgpstrForSceneTextRecognition

[[autodoc]] MgpstrForSceneTextRecognition
    - forward

<!--Copyright 2023 The HuggingFace Team. All rights reserved.

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.

⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
rendered properly in your Markdown viewer.

-->

# MGP-STR

## Overview

The MGP-STR model was proposed in [Multi-Granularity Prediction for Scene Text Recognition](https://arxiv.org/abs/2209.03592) by Peng Wang, Cheng Da, and Cong Yao. MGP-STR is a conceptually **simple** yet **powerful** vision Scene Text Recognition (STR) model, which is built upon the [Vision Transformer (ViT)](vit). To integrate linguistic knowledge, Multi-Granularity Prediction (MGP) strategy is proposed to inject information from the language modality into the model in an implicit way.

The abstract from the paper is the following:

*Scene text recognition (STR) has been an active research topic in computer vision for years. To tackle this challenging problem, numerous innovative methods have been successively proposed and incorporating linguistic knowledge into STR models has recently become a prominent trend. In this work, we first draw inspiration from the recent progress in Vision Transformer (ViT) to construct a conceptually simple yet powerful vision STR model, which is built upon ViT and outperforms previous state-of-the-art models for scene text recognition, including both pure vision models and language-augmented methods. To integrate linguistic knowledge, we further propose a Multi-Granularity Prediction strategy to inject information from the language modality into the model in an implicit way, i.e. , subword representations (BPE and WordPiece) widely-used in NLP are introduced into the output space, in addition to the conventional character level representation, while no independent language model (LM) is adopted. The resultant algorithm (termed MGP-STR) is able to push the performance envelop of STR to an even higher level. Specifically, it achieves an average recognition accuracy of 93.35% on standard benchmarks.*

<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/mgp_str_architecture.png"
alt="drawing" width="600"/>

<small> MGP-STR architecture. Taken from the <a href="https://arxiv.org/abs/2209.03592">original paper</a>. </small>

Tips:

- MGP-STR is trained on two synthetic datasets [MJSynth]((http://www.robots.ox.ac.uk/~vgg/data/text/)) (MJ) and SynthText(http://www.robots.ox.ac.uk/~vgg/data/scenetext/) (ST) without fine-tuning on other datasets. It achieves state-of-the-art results on six standard Latin scene text benchmarks, including 3 regular text datasets (IC13, SVT, IIIT) and 3 irregular ones (IC15, SVTP, CUTE).
- This model was contributed by [yuekun](https://huggingface.co/yuekun). The original code can be found [here](https://github.com/AlibabaResearch/AdvancedLiterateMachinery/tree/main/OCR/MGP-STR).

## Inference

[`MgpstrModel`] accepts images as input and generates three types of predictions, which represent textual information at different granularities.
The three types of predictions are fused to give the final prediction result.

The [`ViTImageProcessor`] class is responsible for preprocessing the input image and
[`MgpstrTokenizer`] decodes the generated character tokens to the target string. The
[`MgpstrProcessor`] wraps [`ViTImageProcessor`] and [`MgpstrTokenizer`]
into a single instance to both extract the input features and decode the predicted token ids.

- Step-by-step Optical Character Recognition (OCR)

``` py
>>> from transformers import MgpstrProcessor, MgpstrForSceneTextRecognition
>>> import requests
>>> from PIL import Image

>>> processor = MgpstrProcessor.from_pretrained('alibaba-damo/mgp-str-base')
>>> model = MgpstrForSceneTextRecognition.from_pretrained('alibaba-damo/mgp-str-base')

>>> # load image from the IIIT-5k dataset
>>> url = "https://i.postimg.cc/ZKwLg2Gw/367-14.png"
>>> image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

>>> pixel_values = processor(images=image, return_tensors="pt").pixel_values
>>> outputs = model(pixel_values)

>>> generated_text = processor.batch_decode(outputs.logits)['generated_text']
```

## MgpstrConfig

[[autodoc]] MgpstrConfig

## MgpstrTokenizer

[[autodoc]] MgpstrTokenizer
    - save_vocabulary

## MgpstrProcessor

[[autodoc]] MgpstrProcessor
    - __call__
    - batch_decode

## MgpstrModel

[[autodoc]] MgpstrModel
    - forward

## MgpstrForSceneTextRecognition

[[autodoc]] MgpstrForSceneTextRecognition
    - forward
