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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 Pipeline), 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
• 2020.9.17 update English recognition model and Multilingual recognition model, 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
• 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.

Community

• Scan the QR code below with your Wechat, you can access to official technical exchange group. Look forward to your participation.

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)

• OCR Quick Start

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
  • Text Detection Algorithm
  • Text Recognition Algorithm
  • PP-OCR Pipeline
• Model training/evaluation
  • Text Detection
  • Text Recognition
  • Direction Classification
  • Yml Configuration
• Inference and Deployment
  • Quick inference based on pip
  • Python Inference
  • C++ Inference
  • Serving
  • Mobile
  • Model Quantization
  • Model Compression
  • Benchmark
• Datasets
  • General OCR Datasets(Chinese/English)
  • HandWritten_OCR_Datasets(Chinese)
  • Various OCR Datasets(multilingual)
  • Data Annotation Tools
  • Data Synthesis Tools
• Visualization
• FAQ
• Community
• References
• License
• Contribution

PP-OCR Pipeline

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.

Visualization more

• Chinese OCR model

• English OCR model

• Multilingual OCR model

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.
• Thanks lijinhan for contributing a new way, i.e., java SpringBoot, to achieve the request for the Hubserving deployment.
• Thanks Mejans for contributing the Occitan corpus and character set.