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Mushroomcat9998 / PaddleOCR

Custom repo for training Japanese OCR
Apache License 2.0
23 stars 10 forks source link

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English | 简体中文

Introduction

PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.

Notice

PaddleOCR supports both dynamic graph and static graph programming paradigm

Recent updates

Features

Visualization

The above pictures are the visualizations of the general ppocr_server model. For more effect pictures, please see More visualizations.

Community

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 2.0 series model list（Update on Dec 15）

Note : Compared with models 1.1, which are trained with static graph programming paradigm, models 2.0 are the dynamic graph trained version and achieve close performance.

Model introduction Model name Recommended scene Detection model Direction classifier Recognition model
Chinese and English ultra-lightweight OCR model (9.4M) ch_ppocr_mobile_v2.0_xx Mobile & server inference model / pre-trained model inference model / pre-trained model inference model / pre-trained model
Chinese and English general OCR model (143.4M) ch_ppocr_server_v2.0_xx Server inference model / pre-trained model inference model / pre-trained model inference model / pre-trained model

For more model downloads (including multiple languages), please refer to PP-OCR v2.0 series model downloads.

For a new language request, please refer to Guideline for new language_requests.

Tutorials

PP-OCR Pipeline

PP-OCR is a practical ultra-lightweight OCR system. It is mainly composed of three parts: DB text detection[2], detection frame correction and CRNN text recognition[7]. 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 [8] and PACT quantization [9] is based on PaddleSlim.

Visualization more

Guideline for new language requests

If you want to request a new language support, a PR with 2 following files are needed：

  1. In folder ppocr/utils/dict, it is necessary to submit the dict text to this path and name it with {language}_dict.txt that contains a list of all characters. Please see the format example from other files in that folder.

  2. In folder ppocr/utils/corpus, it is necessary to submit the corpus to this path and name it with {language}_corpus.txt that contains a list of words in your language. Maybe, 50000 words per language is necessary at least. Of course, the more, the better.

If your language has unique elements, please tell me in advance within any way, such as useful links, wikipedia and so on.

More details, please refer to Multilingual OCR Development Plan.

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.