Shows an illustrated sun in light mode and a moon with stars in dark mode.

Open source OCR for the security of the digital world

Join our [Discord](https://discord.gg/33eyQJq498) [简体中文](./README-CN.md) | English

### 📝 Introduction RapidOCR is a completely open-source, free OCR tool that supports multi-platform, multi-language operation and rapid offline deployment. Its core advantages lie in extreme speed and extensive compatibility. **Supported Languages:** Default support for Chinese and English recognition. For other supported languages, please refer to the documentation: [Model List](https://rapidai.github.io/RapidOCRDocs/main/model_list/). **Project Origin:** Considering that [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) still has room for optimization in engineering aspects, we innovatively converted the models in PaddleOCR into the highly compatible ONNX format to simplify and accelerate the inference deployment of OCR models on various terminal devices. Furthermore, we achieved seamless cross-platform porting based on multiple programming languages such as Python, C++, Java, and C#, enabling developers to get started easily and integrate efficiently. **Name Implication:** The name "RapidOCR" embodies our core expectations for the product: **Rapid** (simple operation, fast response), **Good & Economical** (low resource consumption, high cost-effectiveness), and **Intelligent** (achieving accurate and efficient recognition relying on deep learning technology). We focus on leveraging the advantages of artificial intelligence to create compact yet powerful models, relentlessly pursuing speed while ensuring excellent recognition results. **User Guide:** - **Direct Deployment:** If the models provided in this repository meet your needs, simply refer to the [Official Documentation](https://rapidai.github.io/RapidOCRDocs/main/quickstart/) to quickly complete the deployment and usage of RapidOCR. - **Custom Fine-tuning:** If the existing models cannot meet specific scenario requirements, you can fine-tune them using your own data based on PaddleOCR, and then apply the optimized models to the RapidOCR deployment process to achieve personalized customization. If you find this project helpful for your work or study, we kindly ask you to give us a ⭐ Star to provide valuable support and encouragement! ### 🎥 Visualization

### 🛠️ Installation ```bash pip install rapidocr onnxruntime ``` ### 📋 Usage ```python from rapidocr import RapidOCR engine = RapidOCR() img_url = "https://www.modelscope.cn/models/RapidAI/RapidOCR/resolve/master/resources/test_files/ch_en_num.jpg" result = engine(img_url) print(result) result.vis("vis_result.jpg") ``` ### 📚 Documentation Full documentation can be found on [docs](https://rapidai.github.io/RapidOCRDocs/), in Chinese. ### 👥 Who use? ([more](https://github.com/RapidAI/RapidOCR/network/dependents)) - [Docling](https://github.com/DS4SD/docling) - [CnOCR](https://github.com/breezedeus/CnOCR) - [api-for-open-llm](https://github.com/xusenlinzy/api-for-open-llm) - [arknights-mower](https://github.com/ArkMowers/arknights-mower) - [pensieve](https://github.com/arkohut/pensieve) - [genshin_artifact_auxiliary](https://github.com/SkeathyTomas/genshin_artifact_auxiliary) - [ChatLLM](https://github.com/yuanjie-ai/ChatLLM) - [langchain](https://github.com/langchain-ai/langchain) - [Langchain-Chatchat](https://github.com/chatchat-space/Langchain-Chatchat) - [JamAIBase](https://github.com/EmbeddedLLM/JamAIBase) - [PAI-RAG](https://github.com/aigc-apps/PAI-RAG) - [ChatAgent_RAG](https://github.com/junyuyang7/ChatAgent_RAG) - [OpenAdapt](https://github.com/OpenAdaptAI/OpenAdapt) - [Umi-OCR](https://github.com/hiroi-sora/Umi-OCR) > For more projects that use RapidOCR, you are welcome to [register](https://github.com/RapidAI/RapidOCR/discussions/286) at the registration address. Registration is solely for product promotion. ### 🙏 Acknowledgements - Many thanks to [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR) for everything. - Many thanks to [PaddleOCR2Pytorch](https://github.com/frotms/PaddleOCR2Pytorch) for providing the converted PyTorch format models. - Many thanks to [PaddleX](https://github.com/PaddlePaddle/PaddleX) for providing the document models. - Many thanks to [DeliciaLaniD](https://github.com/DeliciaLaniD) for fixing the misplaced start position of scan animation in ocrweb. - Many thanks to [zhsunlight](https://github.com/zhsunlight) for the suggestion about parameterized call GPU reasoning and the careful and thoughtful testing. - Many thanks to [lzh111222334](https://github.com/lzh111222334) for fixing some bugs of rec preprocessing under python version. - Many thanks to [AutumnSun1996](https://github.com/AutumnSun1996) for the suggestion in the [#42](https://github.com/RapidAI/RapidOCR/issues/42). - Many thanks to [DeadWood8](https://github.com/DeadWood8) for providing the [document](https://rapidai.github.io/RapidOCRDocs/main/install_usage/rapidocr_web/nuitka_package/) which packages rapidocr_web to exe by Nuitka. - Many thanks to [Loovelj](https://github.com/Loovelj) for fixing the bug of sorting the text boxes. For details see [issue 75](https://github.com/RapidAI/RapidOCR/issues/75). ### 🤝 Contribution Guide This repository contains the **Python** component of RapidOCR. Components for other languages have been migrated to separate repositories. For the complete workflow on contributing to Python development, please refer to: [**Python CONTRIBUTING**](docs/CONTRIBUTING.md). ### 🎖 Code Contributors

### 🌟 Sponsors & Backers RapidOCR is an Apache2.0-licensed open source project with its ongoing development made possible entirely by the support of these awesome backers. If you'd like to join them, please consider [sponsoring RapidOCR's development](https://rapidai.github.io/RapidOCRDocs/main/sponsor/). #### Sponsors |Sponsors|Application|Introduction| |:---:|:---:|:---| |

|[Quicker](https://getquicker.net/)|Your fingertip toolbox| #### Backers

_@zg-young

_@Eunsolfs

_@youzzhang

### 📜 Citation If you find this project useful in your research, please consider cite: ```bibtex @misc{RapidOCR 2021, title={{Rapid OCR}: OCR Toolbox}, author={RapidAI Team}, howpublished = {\url{https://github.com/RapidAI/RapidOCR}}, year={2021} } ``` ### ⭐️ Stargazers over time [![Stargazers over time](https://starchart.cc/RapidAI/RapidOCR.svg?variant=adaptive)](https://starchart.cc/RapidAI/RapidOCR) ### ⚖️ License The copyright of the OCR model is held by Baidu, while the copyrights of all other engineering scripts are retained by the repository's owner. This project is released under the [Apache 2.0 license](./LICENSE).