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SWHL 341f23436b Regenerate rapidocr-openvino whl

2022-09-01 11:05:21 +08:00

change_log_en.md

[Fix] the inference of cls by the openvino.

2022-09-01 09:10:50 +08:00

change_log.md

[Fix] the inference of cls by the openvino.

2022-09-01 09:10:50 +08:00

doc_whl_en_onnxruntime.md

Add rapidocr_openvino whl

2022-09-01 10:48:32 +08:00

doc_whl_en_openvino.md

Regenerate rapidocr-openvino whl

2022-09-01 11:05:21 +08:00

FAQ.md

Update FAQ

2022-07-13 18:53:17 +08:00

README_en.md

[Fix] the inference of cls by the openvino.

2022-09-01 09:10:50 +08:00

related_projects.md

Update related project

2022-07-20 21:22:01 +08:00

README_en.md

RapidOCR

简体中文 | English

Contents

RapidOCR
- Introduction
- Recently updates(more)
- Overall Framework
- FAQ
- SDK compilation status
- Online demo
- Directory structure
- Current Progress
- Model related
  - [Download models(Baidu NetDisk | Google Drive)](#download-modelsbaidu-netdisk--google-drive)
  - Model to onnx
  - Compared
    - Text Det
    - Text Recognition
- Original initiator and start-up author
- Acknowledgements
- Sponsor
- Authorization
- Contact us
- Demo

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(more)

🛴2022-09-01 update:

Since openvino released version 2022.2.0.dev20220829, this version solves the problem of cls partial model inference. So far, the openvino-based rapidocr has been unified, and it is all completed by the openvino inference engine.
For detailed usage, see: python/README.

🧸2022-08-17 update:

The python/ocrweb part v1.1.0 is released, see the link for details.

🕶2022-08-14 update:

The ocrweb part adds the function of deploying calls by API, and you can send POST requests to get OCR recognition results.
For details, see: API deploy

Overall Framework

flowchart LR
    A[/OurSelf Dataset/] --> B(PaddleOCR)
    B --train --> C(Text Det) & D(Text Cls) & E(Text Rec) --Convert--> F(ONNX)
    F --> G{RapidOCR Deploy\n<b>Python/C++/Java/C#</b>}
    G --> H(Windows x86/x64) & I(Linux) & J(Android) & K(Web) & L(Raspberry Pi)

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

The model combination (optimal combination) used for the demo is: ch_PP-OCRv3_det + ch_ppocr_mobile_v2.0_cls + ch_PP-OCRv3_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
    ├── docs            # some docs
    ├── dotnet          # .Net program directory
    ├── 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

Download models(Baidu NetDisk | Google Drive)

Model to onnx

Compared

Text Det

test dataset: Chinese and English (111, including cards, documents and natural images)

Model	infer_Speed(s/img)	precision	recall	hmean	Model Size
ch_ppocr_mobile_v2.0_det_infer.onnx	0.4345742	0.7277	0.8413	0.7785	2.3M
ch_PP-OCRv2_det_infer.onnx	0.5116553	0.7817	0.8472	0.8123	2.3M
ch_PP-OCRv3_det_infer.onnx	0.5723512	0.7740	0.8837	0.8237	2.4M

Text Recognition

test dataset: Chinese and English (168)

Model	infer_speed(s/img)	Score	Exact_Match	Char_Match	Model Size
ch_ppocr_mobile_v2.0_rec_infer.onnx	0.0111	0.7287	0.5595	0.8979	4.3M
ch_PP-OCRv2_rec_infer.onnx	0.0193	0.6955	0.4881	0.9029	8.0M
ch_PP-OCRv3_rec_infer.onnx	0.0145	0.5537	0.3274	0.7800	11M
ch_PP-OCRv3_rec_train_student.onnx	0.0157	0.5537	0.3274	0.7800	11M
ch_PP-OCRv3_rec_train_teacher.onnx	0.0140	0.5381	0.3095	0.7667	11M

Original initiator and start-up author

Acknowledgements

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 suggestiong in the #42.

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.

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.