English | 简体中文
PaddleOCR aims to create multilingual, awesome, leading, and practical OCR tools that help users train better models and apply them into practice.
-
🔥2022.8.24 Release PaddleOCR release/2.6
- Release PP-Structurev2,with functions and performance fully upgraded, adapted to Chinese scenes, and new support for Layout Recovery and PDF to Word;
- Layout Analysis optimization: model storage reduced by 95%, while speed increased by 11 times, and the average CPU time-cost is only 41ms;
- Table Recognition optimization: 3 optimization strategies are designed, and the model accuracy is improved by 6% under comparable time consumption;
- Key Information Extraction optimization:a visual-independent model structure is designed, the accuracy of semantic entity recognition is increased by 2.8%, and the accuracy of relation extraction is increased by 9.1%.
-
🔥2022.5.9 Release PaddleOCR release/2.5
- Release PP-OCRv3: With comparable speed, the effect of Chinese scene is further improved by 5% compared with PP-OCRv2, the effect of English scene is improved by 11%, and the average recognition accuracy of 80 language multilingual models is improved by more than 5%.
- Release PPOCRLabelv2: Add the annotation function for table recognition task, key information extraction task and irregular text image.
- Release interactive e-book "Dive into OCR", covers the cutting-edge theory and code practice of OCR full stack technology.
PaddleOCR support a variety of cutting-edge algorithms related to OCR, and developed industrial featured models/solution PP-OCR and PP-Structure on this basis, and get through the whole process of data production, model training, compression, inference and deployment.
It is recommended to start with the “quick experience” in the document tutorial
- Web online experience for 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
- One line of code quick use: Quick Start
- Join us👬: Scan the QR code below with your Wechat, you can join the official technical discussion group. Looking forward to your participation.
Model introduction | Model name | Recommended scene | Detection model | Direction classifier | Recognition model |
---|---|---|---|---|---|
Chinese and English ultra-lightweight PP-OCRv3 model(16.2M) | ch_PP-OCRv3_xx | Mobile & Server | inference model / trained model | inference model / trained model | inference model / trained model |
English ultra-lightweight PP-OCRv3 model(13.4M) | en_PP-OCRv3_xx | Mobile & Server | inference model / trained model | inference model / trained model | inference model / trained 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.
- For structural document analysis models, please refer to PP-Structure models.
- Environment Preparation
- PP-OCR 🔥
- PP-Structure 🔥
- Academic Algorithms
- Data Annotation and Synthesis
- Datasets
- Code Structure
- Visualization
- Community
- New language requests
- FAQ
- References
- License
Visualization more
PP-Structurev2
- layout analysis + table recognition
- SER (Semantic entity recognition)
- RE (Relation Extraction)
If you want to request a new language support, a PR with 1 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.
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