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configs | ||
deploy | ||
doc | ||
ppocr | ||
tools | ||
train_data | ||
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.gitignore | ||
.pre-commit-config.yaml | ||
.style.yapf | ||
LICENSE | ||
MANIFEST.in | ||
README.md | ||
README_ch.md | ||
__init__.py | ||
paddleocr.py | ||
requirments.txt | ||
setup.py |
README.md
English | 简体中文
Introduction
PaddleOCR aims to create rich, leading, and practical OCR tools that help users train better models and apply them into practice.
Recent updates
- 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 Pipline), suitable for mobile deployment. Model Downloads
- 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
- 2020.8.24 Support the use of PaddleOCR through whl package installation,pelease refer PaddleOCR Package
- 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
- more
Features
- 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
Visualization
The above pictures are the visualizations of the general ppocr_server model. For more effect pictures, please see More visualizations.
Quick Experience
You can also quickly experience the ultra-lightweight OCR : Online Experience
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
Also, you can scan the QR code below to install the App (Android support only)
PP-OCR 1.1 series model list(Update on Sep 17)
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 / pre-trained model | inference model / pre-trained model | inference model / pre-trained model |
Chinese and English general OCR model (155.1M) | ch_ppocr_server_v1.1_xx | Server | inference model / pre-trained model | inference model / pre-trained model | inference model / pre-trained model |
Chinese and English ultra-lightweight compressed OCR model (3.5M) | ch_ppocr_mobile_slim_v1.1_xx | Mobile | inference model / slim model | inference model / slim model | inference model / slim model |
For more model downloads (including multiple languages), please refer to PP-OCR v1.1 series model downloads
Tutorials
- Installation
- Quick Start
- Code Structure
- Algorithm introduction
- Model training/evaluation
- Inference and Deployment
- Datasets
- Visualization
- FAQ
- Community
- References
- License
- Contribution
PP-OCR Pipline
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).
Visualization more
Community
Scan the QR code below with your Wechat and completing the questionnaire, you can access to offical technical exchange group.
License
This project is released under Apache 2.0 license
Contribution
We welcome all the contributions to PaddleOCR and appreciate for your feedback very much.
- Many thanks to Khanh Tran and Karl Horky for contributing and revising the English documentation.
- Many thanks to zhangxin for contributing the new visualize function、add .gitgnore and discard set PYTHONPATH manually.
- Many thanks to lyl120117 for contributing the code for printing the network structure.
- Thanks xiangyubo for contributing the handwritten Chinese OCR datasets.
- Thanks authorfu for contributing Android demo and xiadeye contributing iOS demo, respectively.
- Thanks BeyondYourself for contributing many great suggestions and simplifying part of the code style.
- Thanks tangmq for contributing Dockerized deployment services to PaddleOCR and supporting the rapid release of callable Restful API services.