dtiku-cn / rapidocr Goto Github PK

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Open source OCR for the security of the digital world

• Completely open source, free and support offline deployment of multi-platform and multi-language OCR.
• 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.
• Usage:
  • If the existing model in the repo meets the requirements → RapidOCR deployment can be used.
  • Not meeting requirements → Based on PaddleOCR. Fine-tune your own data → RapidOCR deployment. -If this repo is helpful to you, please click on a small star ⭐ Bah!

• Wiki
  • Support Language
  • ONNXRuntime inference optim
  • RapidOCR Optim
  • config.yaml parameter
• Python demo
• C++ demo(Windows/Linux/macOS)
  • RapidOcrOnnx
  • RapidOcrNcnn
• Jvm demo(Java/Kotlin)
  • RapidOcrOnnxJvm
  • RapidOcrNcnnJvm
• .Net demo(C#)
• Android demo
• API
• Web demo:
  • Web OCR
  • Nuitka package rapdocr_web
  • Multi Web OCR
• RapidStructure
  • orientation
  • layout
  • table recovery
• Derivatives
  • RapidOCR HTTP service/win32 program/easy language writing
• Related projects
  • RapidOCRPDF：extract PDF content.
  • RapidVideOCR: Extract hard subtitles in videos based on RapidOCR
  • LGPMA_Infer: table structure restoration | blog interpretation papers and source code
  • Document Unwarping-PaperEdge | Demo
  • Text Removal-CTRNet | Demo
• Model Convert
  • PaddleOCRModelConverter
  • Paddle2OnnxConvertor
  • Teach you to use ONNXRunTime to deploy PP-OCR
• About model
  • Model Convert
  • Model Download
• FAQ

• Decouple the API from ocrweb and maintain it as a separate module. For details, see API
• After rapidocr_web>0.1.6, pip install rapidocr_web[api] will not be supported for installation, you can directly install and use pip install rapidocr_api.

• Add desktop version of RapidOCRWeb, for details, please refer to RapidOCRWeb desktop version tutorial

• Add the return value of the interface version's return coordinate box (issue #85)
• API mode adds base64 format input
• For details, please refer to: link

flowchart LR
    subgraph Step
    direction TB
    C(Text Det) --> D(Text Cls) --> E(Text Rec)
    end

    A[/OurSelf Dataset/] --> B(PaddleOCR) --Train--> Step --> F(PaddleOCRModelConverter)
    F --ONNX--> G{RapidOCR Deploy\n<b>Python/C++/Java/C#</b>}
    G --> H(Windows x86/x64) & I(Linux) & J(Android) & K(Web) & L(Raspberry Pi)

    click B "https://github.com/PaddlePaddle/PaddleOCR" _blank
    click F "https://github.com/RapidAI/PaddleOCRModelConverter" _blank

• Online demo
  • For details, please refer to: ocrweb/README
  • The model combination (optimal combination) used for the demo is:

    ch_PP-OCRv3_det + ch_ppocr_mobile_v2.0_cls + ch_PP-OCRv3_rec
    

  • Demo:
    
• Hugging Face Demo
  • The demo is built on Hugging Face's Spaces, generated by the Gradio library.
  • Demo:
    

• See here: link

• Many thanks to DeliciaLaniD for fixing the misplaced start position of scan animation in ocrweb.
• Many thanks to zhsunlight for the suggestion about parameterized call GPU reasoning and the careful and thoughtful testing.
• Many thanks to lzh111222334 for fixing some bugs of rec preprocessing under python version.
• Many thanks to AutumnSun1996 for the suggestion in the #42.
• Many thanks to DeadWood8 for providing the document which packages rapidocr_web to exe by Nuitka.
• Many thanks to Loovelj for fixing the bug of sorting the text boxes. For details see issue 75.

Sponsor	Applied Products
	-

• If you want to sponsor the project, you can directly click the Sponsor button at the top of the current page, please write a note (e.g. your github account name) to facilitate adding to the sponsorship list above.

• 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 Apache 2.0. You are welcome to contribute code, submit an issue or even PR.
• If you find this project useful in your research, please consider citing:

  @misc{RapidOCR 2021,
      title={{Rapid OCR}: OCR Toolbox},
      author={MindSpore Team},
      howpublished = {\url{https://github.com/RapidAI/RapidOCR}},
      year={2021}
  }

• For international developers, we regard RapidOCR Disscussions as our international community platform. All ideas and questions can be discussed here in English.