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PaddleOCR aims to create rich, leading, and practical OCR tools that help users train better models and apply them into practice.
Koreanhave been supported. Models for more languages will continue to be updated.
The above pictures are the visualizations of the general ppocr_server model. For more effect pictures, please see More visualizations.
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)
|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
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.
This project is released under Apache 2.0 license
We welcome all the contributions to PaddleOCR and appreciate for your feedback very much.