188 lines
13 KiB
Markdown
188 lines
13 KiB
Markdown
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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## Notice
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PaddleOCR supports both dynamic graph and static graph programming paradigm
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- Dynamic graph: dygraph branch (default), **supported by paddle 2.0.0 ([installation](./doc/doc_en/installation_en.md))**
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- Static graph: develop branch
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**Recent updates**
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- 2021.1.21 update more than 25+ multilingual recognition models [models list](./doc/doc_en/models_list_en.md), including：English, Chinese, German, French, Japanese，Spanish，Portuguese Russia Arabic and so on. Models for more languages will continue to be updated [Develop Plan](https://github.com/PaddlePaddle/PaddleOCR/issues/1048).
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- 2020.12.15 update Data synthesis tool, i.e., [Style-Text](./StyleText/README.md)，easy to synthesize a large number of images which are similar to the target scene image.
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- 2020.11.25 Update a new data annotation tool, i.e., [PPOCRLabel](./PPOCRLabel/README.md), which is helpful to improve the labeling efficiency. Moreover, the labeling results can be used in training of the PP-OCR system directly.
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- 2020.9.22 Update the PP-OCR technical article, https://arxiv.org/abs/2009.09941
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- [more](./doc/doc_en/update_en.md)
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## Features
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- PPOCR series of high-quality pre-trained models, comparable to commercial effects
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- Ultra lightweight ppocr_mobile series models: detection (3.0M) + direction classifier (1.4M) + recognition (5.0M) = 9.4M
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- General ppocr_server series models: detection (47.1M) + direction classifier (1.4M) + recognition (94.9M) = 143.4M
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- Support Chinese, English, and digit recognition, vertical text recognition, and long text recognition
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- Support multi-language recognition: Korean, Japanese, German, French
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- Rich toolkits related to the OCR areas
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- Semi-automatic data annotation tool, i.e., PPOCRLabel: support fast and efficient data annotation
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- Data synthesis tool, i.e., Style-Text: easy to synthesize a large number of images which are similar to the target scene image
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- Support user-defined training, provides rich predictive inference deployment solutions
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- Support PIP installation, easy to use
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- Support Linux, Windows, MacOS and other systems
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## Visualization
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<div align="center">
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<img src="doc/imgs_results/ch_ppocr_mobile_v2.0/test_add_91.jpg" width="800">
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<img src="doc/imgs_results/multi_lang/img_01.jpg" width="800">
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<img src="doc/imgs_results/multi_lang/img_02.jpg" width="800">
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</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>
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## Community
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- Scan the QR code below with your Wechat, you can access to official technical exchange group. Look forward to your participation.
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<div align="center">
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<img src="https://raw.githubusercontent.com/PaddlePaddle/PaddleOCR/dygraph/doc/joinus.PNG" width = "200" height = "200" />
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</div>
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## 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)
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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)
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Also, you can scan the QR code below to install the App (**Android support only**)
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<div align="center">
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<img src="./doc/ocr-android-easyedge.png" width = "200" height = "200" />
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</div>
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- [**OCR Quick Start**](./doc/doc_en/quickstart_en.md)
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<a name="Supported-Chinese-model-list"></a>
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## PP-OCR 2.0 series model list（Update on Dec 15）
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**Note** : Compared with [models 1.1](https://github.com/PaddlePaddle/PaddleOCR/blob/develop/doc/doc_en/models_list_en.md), which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.
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| Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model |
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| ------------------------------------------------------------ | ---------------------------- | ----------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| Chinese and English ultra-lightweight OCR model (9.4M) | ch_ppocr_mobile_v2.0_xx | Mobile & server |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_det_train.tar)|[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_rec_pre.tar) |
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| Chinese and English general OCR model (143.4M) | ch_ppocr_server_v2.0_xx | Server |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_det_train.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_traingit.tar) |[inference model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_infer.tar) / [pre-trained model](https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_server_v2.0_rec_pre.tar) |
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For more model downloads (including multiple languages), please refer to [PP-OCR v2.0 series model downloads](./doc/doc_en/models_list_en.md).
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For a new language request, please refer to [Guideline for new language_requests](#language_requests).
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## Tutorials
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- [Installation](./doc/doc_en/installation_en.md)
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- [Quick Start](./doc/doc_en/quickstart_en.md)
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- [Code Structure](./doc/doc_en/tree_en.md)
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- Algorithm Introduction
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- [Text Detection Algorithm](./doc/doc_en/algorithm_overview_en.md)
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- [Text Recognition Algorithm](./doc/doc_en/algorithm_overview_en.md)
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- [PP-OCR Pipeline](#PP-OCR-Pipeline)
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- Model Training/Evaluation
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- [Text Detection](./doc/doc_en/detection_en.md)
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- [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
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- [Quick Inference Based on PIP](./doc/doc_en/whl_en.md)
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- [Python Inference](./doc/doc_en/inference_en.md)
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- [C++ Inference](./deploy/cpp_infer/readme_en.md)
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- [Serving](./deploy/pdserving/README.md)
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- [Mobile](./deploy/lite/readme_en.md)
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- [Benchmark](./doc/doc_en/benchmark_en.md)
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- Data Annotation and Synthesis
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- [Semi-automatic Annotation Tool: PPOCRLabel](./PPOCRLabel/README.md)
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- [Data Synthesis Tool: Style-Text](./StyleText/README.md)
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- [Other Data Annotation Tools](./doc/doc_en/data_annotation_en.md)
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- [Other Data Synthesis Tools](./doc/doc_en/data_synthesis_en.md)
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- Datasets
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- [General OCR Datasets(Chinese/English)](./doc/doc_en/datasets_en.md)
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- [HandWritten_OCR_Datasets(Chinese)](./doc/doc_en/handwritten_datasets_en.md)
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- [Various OCR Datasets(multilingual)](./doc/doc_en/vertical_and_multilingual_datasets_en.md)
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- [Visualization](#Visualization)
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- [New language requests](#language_requests)
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- [FAQ](./doc/doc_en/FAQ_en.md)
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- [Community](#Community)
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- [References](./doc/doc_en/reference_en.md)
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- [License](#LICENSE)
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- [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">
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<img src="./doc/ppocr_framework.png" width="800">
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</div>
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PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection[2], detection frame correction and CRNN text recognition[7]. 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 [8] and PACT quantization [9] is based on [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
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## Visualization [more](./doc/doc_en/visualization_en.md)
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- Chinese OCR model
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<div align="center">
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<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/test_add_91.jpg" width="800">
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<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/00015504.jpg" width="800">
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<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/00056221.jpg" width="800">
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<img src="./doc/imgs_results/ch_ppocr_mobile_v2.0/rotate_00052204.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/ch_ppocr_mobile_v2.0/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/french_0.jpg" width="800">
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<img src="./doc/imgs_results/korean.jpg" width="800">
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</div>
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<a name="language_requests"></a>
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## 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](./ppocr/utils/dict),
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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](./ppocr/utils/corpus),
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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.
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Maybe, 50000 words per language is necessary at least.
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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>
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## License
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This project is released under <a href="https://github.com/PaddlePaddle/PaddleOCR/blob/master/LICENSE">Apache 2.0 license</a>
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<a name="CONTRIBUTION"></a>
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## Contribution
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We welcome all the contributions to PaddleOCR and appreciate for your feedback very much.
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- Many thanks to [Khanh Tran](https://github.com/xxxpsyduck) and [Karl Horky](https://github.com/karlhorky) for contributing and revising the English documentation.
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- Many thanks to [zhangxin](https://github.com/ZhangXinNan) for contributing the new visualize function、add .gitignore and discard set PYTHONPATH manually.
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- Many thanks to [lyl120117](https://github.com/lyl120117) for contributing the code for printing the network structure.
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- Thanks [xiangyubo](https://github.com/xiangyubo) for contributing the handwritten Chinese OCR datasets.
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- Thanks [authorfu](https://github.com/authorfu) for contributing Android demo and [xiadeye](https://github.com/xiadeye) contributing iOS demo, respectively.
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- Thanks [BeyondYourself](https://github.com/BeyondYourself) for contributing many great suggestions and simplifying part of the code style.
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- 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.
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- Thanks [Mejans](https://github.com/Mejans) for contributing the Occitan corpus and character set.
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- Thanks [LKKlein](https://github.com/LKKlein) for contributing a new deploying package with the Golang program language.
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- 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。
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