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PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.
Recent updates
2021.12.21 OCR open source online course starts. The lesson starts at 8:30 every night and lasts for ten days. Free registration: https://aistudio.baidu.com/aistudio/course/introduce/25207
2021.12.21 release PaddleOCR v2.4, release 1 text detection algorithm (PSENet), 3 text recognition algorithms (NRTR、SEED、SAR), 1 key information extraction algorithm (SDMGR, tutorial) and 3 DocVQA algorithms (LayoutLM, LayoutLMv2, LayoutXLM, tutorial).
PaddleOCR R&D team would like to share the key points of PP-OCRv2, at 20:15 pm on September 8th, Course Address.
2021.9.7 release PaddleOCR v2.3, PP-OCRv2 is proposed. The inference speed of PP-OCRv2 is 220% higher than that of PP-OCR server in CPU device. The F-score of PP-OCRv2 is 7% higher than that of PP-OCR mobile.
2021.8.3 released PaddleOCR v2.2, add a new structured documents analysis toolkit, i.e., PP-Structure, support layout analysis and table recognition (One-key to export chart images to Excel files).
2021.4.8 release end-to-end text recognition algorithm PGNet which is published in AAAI 2021. Find tutorial here;release multi language recognition models, support more than 80 languages recognition; especically, the performance of English recognition model is Optimized.
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 PP-OCRv2 model(11.6M) | ch_PP-OCRv2_xx | Mobile & Server | inference model / trained model | inference model / trained model | inference model / trained model |
Chinese and English ultra-lightweight PP-OCR model (9.4M) | ch_ppocr_mobile_v2.0_xx | Mobile & server | inference model / trained model | inference model / trained model | inference model / trained model |
Chinese and English general PP-OCR model (143.4M) | ch_ppocr_server_v2.0_xx | Server | inference model / trained model | inference model / trained model | inference model / trained model |
For more model downloads (including multiple languages), please refer to PP-OCR series model downloads.
For a new language request, please refer to Guideline for new language_requests.
[1] 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 (as shown in the green box above). 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).
[2] On the basis of PP-OCR, PP-OCRv2 is further optimized in five aspects. The detection model adopts CML(Collaborative Mutual Learning) knowledge distillation strategy and CopyPaste data expansion strategy. The recognition model adopts LCNet lightweight backbone network, U-DML knowledge distillation strategy and enhanced CTC loss function improvement (as shown in the red box above), which further improves the inference speed and prediction effect. For more details, please refer to the technical report of PP-OCRv2 (arXiv link is coming soon).
If you want to request a new language support, a PR with 2 following files are needed:
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.
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.
This project is released under Apache 2.0 license
We welcome all the contributions to PaddleOCR and appreciate for your feedback very much.
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