RapidOCR

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Contents

• RapidOCR
  • Introduction
  • Recently updates
    • 🎧2022-04-04 update
    • 2022-02-24 update
    • 2021-12-18 update
    • 2021-11-28 update
    • 2021-11-13 update
    • 2021-10-27 update
    • 2021-09-13 update
    • 2021-09-11 update
    • 2021-08-07 update
    • 2021-07-17 update
    • 2021-07-04 update
    • 2021-06-20 update
    • 2021-06-10 update
    • 2021-06-08 update
    • 2021-03-24 update
  • FAQ
  • SDK compilation status
  • Online demo
  • Directory structure
  • Current Progress
  • Model related
    • Download models
    • Model to onnx
  • PaddleOCR-FAQ
  • Original initiator and start-up author
  • Authorization
  • Contact us
  • Demo
    • Demonstration with C++/JVM
    • Demonstration with .Net
    • Demonstratioin with multi_language

Introduction

• Completely open source, free and support offline deployment of multi-platform and multi-language OCR SDK

• Chinese Advertising: Welcome to join our QQ group to download the model and test program, QQ group number: 887298230

• Cause: Baidu paddlepaddle engineering is not very good, in order to facilitate everyone to perform OCR reasoning on various terminals, we convert it to onnx format, use ``python/c++/java/swift/c#'' to change It is ported to various platforms.

• Name Source: Light, fast, economical and smart. OCR technology based on deep learning technology focuses on artificial intelligence advantages and small models, with speed as the mission and effect as the leading role.

• Based on Baidu's open source PaddleOCR model and training, anyone can use this inference library, or use Baidu's paddlepaddle framework for model optimization according to their own needs.

Recently updates

🎧2022-04-04 update

• Add suport for OpenVINO under python
• Give the performance comparison table of OpenVINO and ONNXRuntime
• For details:python/README

2022-02-24 update

• Optimize the inference code of the python part.
• Add inference code examples that use the different language models.
• For details, see: python/README

2021-12-18 update

• Add Google Colab Demo.
• Change the default det model of the python/rapidOCR.sh

2021-11-28 update

• Update the ocrweb part
  • Add the display of the inference time of each stage.
  • Add docs of the ocrweb.
  • Change the det model(ch_PP-OCRv2_det_infer.onnx), faster and more accurate.

Previous update logs

2021-11-13 update

• Add adjustable super parameters for text detection and recognition in Python version, mainly box_thresh|unclip_ratio|text_score, see parameter adjustment for details
• The dictionary position in text recognition is given in parameter mode to facilitate flexible configuration. See keys_path for details

2021-10-27 update

• Add the code that uses the onnxruntime GPU version of infering follow the official tutorial Configuration. (however, the onnxruntime GPU version is not stable to use)

• See: python/README.md for specific steps.

2021-09-13 update

• Add a whl file based on Python for ease of use. See release/python for details.

2021-09-11 update

• Add PP-OCRv2 new model onnx version.

• The infering code of the method is unchanged, and the corresponding model can be directly replaced.

• After evaluation on its own test set:

  • The effect of PP-OCRv2 detection model has been greatly improved, and the model size has not changed.
  • The effect of PP-OCRv2 recognition model was not significantly improved, and the model size increased by 3.58M.

• Upload the model to Baidu online disk extraction code: 30jv or Google Drive

2021-08-07 update

• TODO:

  • PP structure table structure and cell coordinate prediction are being sorted out.

• Previously done, unfinished, welcome to PR

  • make dokcer image
  • try onnxruntime GPU reasoning

2021-07-17 update

• Improve the README document
• Add English, number recognitiononnx model, please refer to python/en_number_ppocr_mobile_v2_rec for details, the usage is the same as others
• Organize Model to onnx

2021-07-04 update

• The python program under the repository can be successfully run on the Raspberry Pi 4B. For more information, please enter the QQ group and ask the group owner
• Update the overall structure diagram and add support for Raspberry Pi

2021-06-20 update

• Optimize the display of recognition results in ocrweb, and add recognition animations to demonstrate at the same time
• Update the datasets directory, add some commonly used database links

2021-06-10 update

• Add server version text recognition model, see details Extract code：30jv

2021-06-08 update

• Organize the warehouse and unify the model download path
• Improve related documentation

2021-03-24 update

• The new model is fully compatible with ONNXRuntime 1.7 or higher. Special thanks: @Channingss
• The performance of the new version of onnxruntime is improved by more than 40% compared to 1.6.0.

FAQ

SDK compilation status

Since ubuntu users are all commercial users and have the ability to compile, pre-compiled packages are not provided for the time being, and they can be compiled by themselves.

Platform	Compilation Status	Offer Status
Windows x86/x64		Download Link
Linux x64		Not available yet, compile by yourself

Online demo

• Web demo
• The model combination used in the demo is: ch_PP-OCRv2 det + mobile cls + mobile rec
• Environment: 4 Core AMD EPYC 7K62 48-Core Processor
• Gif Demo:
  

Directory structure

click to expand

RapidOCR
    ├── android         # Android project directory
    ├── api4cpp         # C language cross-platform interface library source code directory, directly compile with CMakelists.txt under the root
    ├── assets          # Some pictures for demonstration, not a test set
    ├── commonlib       # common library
    ├── cpp             # C++-based project folder
    ├── datasets        # Additional training datasets
    ├── dotnet          # .Net program directory
    ├── FAQ.md          # Some questions and answers
    ├── images          # Test pictures, two typical test pictures, one is a natural scene, the other is a long text
    ├── include         # The header file directory when compiling the c language interface library
    ├── ios             # Apple mobile phone platform project directory
    ├── jvm             # java-based project directory
    ├── lib             # Compilation library file directory, used to compile the C language interface library. Binary files are not uploaded by default
    ├── ocrweb          # Based on python and Flask web
    ├── python          # python reasoning code directory
    ├── release         #
    └── tools           # Some conversion scripts and the like

Current Progress

• C++ example (Windows/Linux/macOS): demo
• Jvm example (Java/Kotlin): demo
• .Net example (C#): demo
• Android example: demo
• python example: demo
• IOS example: waiting for someone to contribute code
• Rewrite the C++ reasoning code according to the python version to improve the reasoning effect, and add support for gif/tga/webp format pictures

Model related

Download models

• Google Drive link
• (download link: Baidu extract code: 30jv)

  ch_ppocr_mobile_v2.0_det_infer.onnx
  ch_ppocr_mobile_v2.0_cls_infer.onnx
  ch_ppocr_mobile_v2.0_rec_infer.onnx
  
  ch_ppocr_server_v2.0_det_infer.onnx
  ch_ppocr_server_v2.0_rec_infer.onnx
  
  japan_rec_crnn.onnx
  en_number_mobile_v2.0_rec_infer.onnx
  

Model to onnx

• Teach you to use ONNXRunTime to deploy PP-OCR by @Channingss
• ✧✧PaddleOCRModelConverter by @SWHL
• Paddle2OnnxConvertor by @benjaminwan

PaddleOCR-FAQ

Original initiator and start-up author

• benjaminwan
• znsoftm

Authorization

• The copyright of the OCR model belongs to Baidu, and the copyright of other engineering codes belongs to the owner of this warehouse.
• This software is licensed under LGPL. You are welcome to contribute code, submit an issue or even pr.

Contact us

• You can contact us through QQ group: 887298230

• If you can’t find the group number, please click here link to find the organization

• Scan the following QR code with QQ:

  

Demo

Demonstration with C++/JVM

Demonstration with .Net

Demonstratioin with multi_language