README_en.md 12.6 KB
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English | [简体中文](README_ch.md)
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## Introduction
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PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.
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**Recent updates**
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- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README_en.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.
- 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941
- 2020.9.19 Update the ultra lightweight compressed ppocr_mobile_slim series models, the overall model size is 3.5M (see [PP-OCR Pipeline](#PP-OCR-Pipeline)), suitable for mobile deployment. [Model Downloads](#Supported-Chinese-model-list)
- 2020.9.17 Update the ultra lightweight ppocr_mobile series and general ppocr_server series Chinese and English ocr models, which are comparable to commercial effects. [Model Downloads](#Supported-Chinese-model-list)
- 2020.9.17 update [English recognition model](./doc/doc_en/models_list_en.md#english-recognition-model) and [Multilingual recognition model](doc/doc_en/models_list_en.md#english-recognition-model), `English`, `Chinese`, `German`, `French`, `Japanese` and `Korean` have been supported. Models for more languages will continue to be updated.
- 2020.8.24 Support the use of PaddleOCR through whl package installation,please refer  [PaddleOCR Package](./doc/doc_en/whl_en.md)
- 2020.8.21 Update the replay and PPT of the live lesson at Bilibili on August 18, lesson 2, easy to learn and use OCR tool spree. [Get Address](https://aistudio.baidu.com/aistudio/education/group/info/1519)
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- [more](./doc/doc_en/update_en.md)

## Features
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- PPOCR series of high-quality pre-trained models, comparable to commercial effects
    - Ultra lightweight ppocr_mobile series models: detection (2.6M) + direction classifier (0.9M) + recognition (4.6M) = 8.1M
    - General ppocr_server series models: detection (47.2M) + direction classifier (0.9M) + recognition (107M) = 155.1M
    - Ultra lightweight compression ppocr_mobile_slim series models: detection (1.4M) + direction classifier (0.5M) + recognition (1.6M) = 3.5M
- Support Chinese, English, and digit recognition, vertical text recognition, and long text recognition
- Support multi-language recognition: Korean, Japanese, German, French
- Support user-defined training, provides rich predictive inference deployment solutions
- Support PIP installation, easy to use
- Support Linux, Windows, MacOS and other systems
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## Visualization

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<div align="center">
    <img src="doc/imgs_results/1101.jpg" width="800">
    <img src="doc/imgs_results/1103.jpg" width="800">
</div>
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The above pictures are the visualizations of the general ppocr_server model. For more effect pictures, please see [More visualizations](./doc/doc_en/visualization_en.md).
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<a name="Community"></a>
## Community
- Scan the QR code below with your Wechat, you can access to official technical exchange group. Look forward to your participation.

<div align="center">
<img src="./doc/joinus.PNG"  width = "200" height = "200" />
</div>


## Quick Experience
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You can also quickly experience the ultra-lightweight OCR : [Online Experience](https://www.paddlepaddle.org.cn/hub/scene/ocr)

Mobile DEMO experience (based on EasyEdge and Paddle-Lite, supports iOS and Android systems): [Sign in to the website to obtain the QR code for  installing the App](https://ai.baidu.com/easyedge/app/openSource?from=paddlelite)

 Also, you can scan the QR code below to install the App (**Android support only**)

<div align="center">
<img src="./doc/ocr-android-easyedge.png"  width = "200" height = "200" />
</div>

- [**OCR Quick Start**](./doc/doc_en/quickstart_en.md)

<a name="Supported-Chinese-model-list"></a>

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## PP-OCR 1.1 series model list(Update on Sep 17)
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| Model introduction                                           | Model name                   | Recommended scene | Detection model                                              | Direction classifier                                         | Recognition model                                            |
| ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| Chinese and English ultra-lightweight OCR model (8.1M)       | ch_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) |  
| Chinese and English general OCR model (155.1M)               | ch_ppocr_server_v1.1_xx      | Server            | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) | [inference model](link) / [pre-trained model](link) |  
| Chinese and English ultra-lightweight compressed OCR model (3.5M) | ch_ppocr_mobile_slim_v1.1_xx | Mobile            | [inference model](link) / [slim model](link) | [inference model](link) / [slim model](link) |    [inference model](link) / [slim model](link) |
| French ultra-lightweight OCR model (4.6M)       | french_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](link) / [pre-trained model](link) | - | [inference model](link) / [pre-trained model](link)  |
| German ultra-lightweight OCR model (4.6M)       | german_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](link) / [pre-trained model](link) | - |[inference model](link) / [pre-trained model](link) |
| Korean ultra-lightweight OCR model (5.9M)       | korean_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](link) / [pre-trained model](link) | - |[inference model](link) / [pre-trained model](link)|
| Japan ultra-lightweight OCR model (6.2M)       | japan_ppocr_mobile_v1.1_xx      | Mobile & server   | [inference model](link) / [pre-trained model](link) | - |[inference model](link) / [pre-trained model](link)  |
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For more model downloads (including multiple languages), please refer to [PP-OCR v1.1 series model downloads](./doc/doc_en/models_list_en.md).

For a new language request, please refer to [Guideline for new language_requests](#language_requests).
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## Tutorials
- [Installation](./doc/doc_en/installation_en.md)
- [Quick Start](./doc/doc_en/quickstart_en.md)
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- [Code Structure](./doc/doc_en/tree_en.md)
- Algorithm Introduction
    - [Text Detection Algorithm](./doc/doc_en/algorithm_overview_en.md)
    - [Text Recognition Algorithm](./doc/doc_en/algorithm_overview_en.md)
    - [PP-OCR Pipeline](#PP-OCR-Pipeline)
- Model Training/Evaluation
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    - [Text Detection](./doc/doc_en/detection_en.md)
    - [Text Recognition](./doc/doc_en/recognition_en.md)
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    - [Direction Classification](./doc/doc_en/angle_class_en.md)
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    - [Yml Configuration](./doc/doc_en/config_en.md)
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- Inference and Deployment
    - [Quick Inference Based on PIP](./doc/doc_en/whl_en.md)
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    - [Python Inference](./doc/doc_en/inference_en.md)
    - [C++ Inference](./deploy/cpp_infer/readme_en.md)
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    - [Serving](./deploy/hubserving/readme_en.md)
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    - [Mobile](./deploy/lite/readme_en.md)
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    - [Model Quantization](./deploy/slim/quantization/README_en.md)
    - [Model Compression](./deploy/slim/prune/README_en.md)
    - [Benchmark](./doc/doc_en/benchmark_en.md)  
- Data Annotation and Synthesis
    - [Semi-automatic Annotation Tool](./PPOCRLabel/README_en.md)
    - [Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
    - [Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
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- Datasets
    - [General OCR Datasets(Chinese/English)](./doc/doc_en/datasets_en.md)
    - [HandWritten_OCR_Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md)
    - [Various OCR Datasets(multilingual)](./doc/doc_en/vertical_and_multilingual_datasets_en.md)
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- [Visualization](#Visualization)
- [New language requests](#language_requests)
- [FAQ](./doc/doc_en/FAQ_en.md)
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- [Community](#Community)
- [References](./doc/doc_en/reference_en.md)
- [License](#LICENSE)
- [Contribution](#CONTRIBUTION)

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<a name="PP-OCR-Pipeline"></a>
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## PP-OCR Pipeline
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<div align="center">
    <img src="./doc/ppocr_framework.png" width="800">
</div>
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PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection, detection frame correction and CRNN text recognition. The system adopts 19 effective strategies from 8 aspects including backbone network selection and adjustment, prediction head design, data augmentation, learning rate transformation strategy, regularization parameter selection, pre-training model use, and automatic model tailoring and quantization to optimize and slim down the models of each module. The final results are an ultra-lightweight Chinese and English OCR model with an overall size of 3.5M and a 2.8M English digital OCR model. For more details, please refer to the PP-OCR technical article (https://arxiv.org/abs/2009.09941). Besides, The implementation of the FPGM Pruner and PACT quantization is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
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## Visualization [more](./doc/doc_en/visualization_en.md)
- Chinese OCR model
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<div align="center">
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    <img src="./doc/imgs_results/1102.jpg" width="800">
    <img src="./doc/imgs_results/1104.jpg" width="800">
    <img src="./doc/imgs_results/1106.jpg" width="800">
    <img src="./doc/imgs_results/1105.jpg" width="800">
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</div>

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- English OCR model
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<div align="center">
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    <img src="./doc/imgs_results/img_12.jpg" width="800">
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</div>

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- Multilingual OCR model
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<div align="center">
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    <img src="./doc/imgs_results/1110.jpg" width="800">
    <img src="./doc/imgs_results/1112.jpg" width="800">
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</div>


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<a name="language_requests"></a>
## Guideline for new language requests
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If you want to request a new language support, a PR with 2 following files are needed:
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1. In folder [ppocr/utils/dict](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/ppocr/utils/dict),
it is necessary to submit the dict text to this path and name it with `{language}_dict.txt` that contains a list of all characters. Please see the format example from other files in that folder.
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2. In folder [ppocr/utils/corpus](https://github.com/PaddlePaddle/PaddleOCR/tree/develop/ppocr/utils/corpus),
it is necessary to submit the corpus to this path and name it with `{language}_corpus.txt` that contains a list of words in your language.
Maybe, 50000 words per language is necessary at least.
Of course, the more, the better.
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If your language has unique elements, please tell me in advance within any way, such as useful links, wikipedia and so on.
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More details, please refer to [Multilingual OCR Development Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
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<a name="LICENSE"></a>
## License
This project is released under <a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>

<a name="CONTRIBUTION"></a>
## Contribution
We welcome all the contributions to PaddleOCR and appreciate for your feedback very much.

- Many thanks to [Khanh Tran](https://github.com/xxxpsyduck) and [Karl Horky](https://github.com/karlhorky) for contributing and revising the English documentation.
- Many thanks to [zhangxin](https://github.com/ZhangXinNan) for contributing the new visualize function、add .gitgnore and discard set PYTHONPATH manually.
- Many thanks to [lyl120117](https://github.com/lyl120117) for contributing the code for printing the network structure.
- Thanks [xiangyubo](https://github.com/xiangyubo) for contributing the handwritten Chinese OCR datasets.
- Thanks [authorfu](https://github.com/authorfu) for contributing Android demo  and [xiadeye](https://github.com/xiadeye) contributing iOS demo, respectively.
- Thanks [BeyondYourself](https://github.com/BeyondYourself) for contributing many great suggestions and simplifying part of the code style.
- Thanks [tangmq](https://gitee.com/tangmq) for contributing Dockerized deployment services to PaddleOCR and supporting the rapid release of callable Restful API services.
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- Thanks [lijinhan](https://github.com/lijinhan) for contributing a new way, i.e., java SpringBoot, to achieve the request for the Hubserving deployment.
- Thanks [Mejans](https://github.com/Mejans) for contributing the Occitan corpus and character set.
- Thanks [LKKlein](https://github.com/LKKlein) for contributing a new deploying package with the Golang program language.
- Thanks [Evezerest](https://github.com/Evezerest), [ninetailskim](https://github.com/ninetailskim), [edencfc](https://github.com/edencfc), [BeyondYourself](https://github.com/BeyondYourself) and [1084667371](https://github.com/1084667371) for contributing a new data annotation tool, i.e., PPOCRLabel。