Since its initial release, PaddleOCR has gained widespread acclaim across academia, industry, and research communities, thanks to its cutting-edge algorithms and proven performance in real-world applications. It’s already powering popular open-source projects like Umi-OCR, OmniParser, MinerU, and RAGFlow, making it the go-to OCR toolkit for developers worldwide.
On May 20, 2025, the PaddlePaddle team unveiled PaddleOCR 3.0, fully compatible with the official release of the [PaddlePaddle 3.0](https://github.com/PaddlePaddle/Paddle) framework. This update further **boosts text-recognition accuracy**, adds support for **multiple text-type recognition** and **handwriting recognition**, and meets the growing demand from large-model applications for **high-precision parsing of complex documents**. When combined with the **ERNIE 4.5T**, it significantly enhances key-information extraction accuracy. PaddleOCR 3.0 also introduces support for domestic hardware platforms such as **KUNLUNXIN** and **Ascend**.
- 🖼️ Universal-Scene Text Recognition Model [PP-OCRv5](./docs/version3.x/algorithm/PP-OCRv5/PP-OCRv5.en.md): A single model that handles five different text types plus complex handwriting. Overall recognition accuracy has increased by 13 percentage points over the previous generation.
- 🧮 General Document-Parsing Solution [PP-StructureV3](./docs/version3.x/algorithm/PP-StructureV3/PP-StructureV3.en.md): Delivers high-precision parsing of multi-layout, multi-scene PDFs, outperforming many open- and closed-source solutions on public benchmarks.
- 📈 Intelligent Document-Understanding Solution [PP-ChatOCRv4](./docs/version3.x/algorithm/PP-ChatOCRv4/PP-ChatOCRv4.en.md): Natively powered by the WenXin large model 4.5T, achieving 15.7 percentage points higher accuracy than its predecessor.
In addition to providing an outstanding model library, PaddleOCR 3.0 also offers user-friendly tools covering model training, inference, and service deployment, so developers can rapidly bring AI applications to production.
You can [Quick Start](#-quick-start) directly, find comprehensive documentation in the [PaddleOCR Docs](https://paddlepaddle.github.io/PaddleOCR/main/index.html), get support via [Github Issus](https://github.com/PaddlePaddle/PaddleOCR/issues), and explore our OCR courses on [OCR courses on AIStudio](https://aistudio.baidu.com/course/introduce/25207).
## 📣 Recent updates
🔥🔥2025.05.20: Official Release of **PaddleOCR v3.0**, including:
- **PP-OCRv5**: High-Accuracy Text Recognition Model for All Scenarios - Instant Text from Images/PDFs.
1. 🌐 Single-model support for **five** text types - Seamlessly process **Simplified Chinese, Traditional Chinese, Simplified Chinese Pinyin, English** and **Japanse** within a single model.
2. ✍️ Improved **handwriting recognition**: Significantly better at complex cursive scripts and non-standard handwriting.
3. 🎯 **13-point accuracy gain** over PP-OCRv4, achieving state-of-the-art performance across a variety of real-world scenarios.
- **PP-StructureV3**: General-Purpose Document Parsing – Unleash SOTA Images/PDFs Parsing for Real-World Scenarios!
1. 🧮 **High-Accuracy multi-scene PDF parsing**, leading both open- and closed-source solutions on the OmniDocBench benchmark.
2. 🧠 Specialized capabilities include **seal recognition**, **chart-to-table conversion**, **table recognition with nested formulas/images**, **vertical text document parsing**, and **complex table structure analysis**.
- **PP-ChatOCRv4**: Intelligent Document Understanding – Extract Key Information, not just text from Images/PDFs.
1. 🔥 **15.7 % improvement** in key-information extraction on PDF/PNG/JPG files over the previous generation.
2. 💻 Native support for **ERINE4.5 Turbo**, with compatibility for large-model deployments via [PaddleNLP](https://github.com/PaddlePaddle/PaddleNLP), Ollama, vLLM, and more.
3. 🤝 Integrated **PP-DocBee2**, enabling extraction and understanding of printed text, handwriting, seals, tables, charts, and other common elements in complex documents.
<details>
<summary><strong>The history of updates </strong></summary>
- 🔥🔥2025.03.07: Release of **PaddleOCR v2.10**, including:
- **12 new self-developed models:**
- **[Layout Detection series](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/layout_detection.html)**(3 models): PP-DocLayout-L, M, and S -- capable of detecting 23 common layout types across diverse document formats(papers, reports, exams, books, magazines, contracts, etc.) in English and Chinese. Achieves up to **90.4% mAP@0.5** , and lightweight features can process over 100 pages per second.
- **[Formula Recognition series](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/formula_recognition.html)**(2 models): PP-FormulaNet-L and S -- supports recognition of 50,000+ LaTeX expressions, handling both printed and handwritten formulas. PP-FormulaNet-L offers **6% higher accuracy** than comparable models; PP-FormulaNet-S is 16x faster while maintaining similar accuracy.
- **[Table Structure Recognition series](https://paddlepaddle.github.io/PaddleX/latest/en/module_usage/tutorials/ocr_modules/table_structure_recognition.html)**(2 models): SLANeXt_wired and SLANeXt_wireless -- newly developed models with **6% accuracy improvement** over SLANet_plus in complex table recognition.
First, please install PaddlePaddle using the official [Installation Guide](https://www.paddlepaddle.org.cn/en/install/quick?docurl=/documentation/docs/en/develop/install/pip/linux-pip_en.html).
💗 PaddleOCR wouldn’t be where it is today without its incredible community! A massive 🙌 thank you 🙌 to all our longtime partners, new collaborators, and everyone who’s poured their passion into PaddleOCR — whether we’ve named you or not. Your support fuels our fire! 🔥
| Project Name | Description |
| ------------ | ----------- |
| [RAGFlow](https://github.com/infiniflow/ragflow) <ahref="https://github.com/infiniflow/ragflow"><imgsrc="https://img.shields.io/github/stars/infiniflow/ragflow"></a>|RAG engine based on deep document understanding.|
| [MinerU](https://github.com/opendatalab/MinerU) <ahref="https://github.com/opendatalab/MinerU"><imgsrc="https://img.shields.io/github/stars/opendatalab/MinerU"></a>|Multi-type Document to Markdown Conversion Tool|
| [OmniParser](https://github.com/microsoft/OmniParser)<ahref="https://github.com/microsoft/OmniParser"><imgsrc="https://img.shields.io/github/stars/microsoft/OmniParser"></a> |OmniParser: Screen Parsing tool for Pure Vision Based GUI Agent.|
| [QAnything](https://github.com/netease-youdao/QAnything)<ahref="https://github.com/netease-youdao/QAnything"><imgsrc="https://img.shields.io/github/stars/netease-youdao/QAnything"></a> |Question and Answer based on Anything.|
| [PDF-Extract-Kit](https://github.com/opendatalab/PDF-Extract-Kit) <ahref="https://github.com/opendatalab/PDF-Extract-Kit"><imgsrc="https://img.shields.io/github/stars/opendatalab/PDF-Extract-Kit"></a>|A powerful open-source toolkit designed to efficiently extract high-quality content from complex and diverse PDF documents.|
| [Dango-Translator](https://github.com/PantsuDango/Dango-Translator)<ahref="https://github.com/PantsuDango/Dango-Translator"><imgsrc="https://img.shields.io/github/stars/PantsuDango/Dango-Translator"></a> |Recognize text on the screen, translate it and show the translation results in real time.|
| [Learn more projects](./awesome_projects.md) | [More projects based on PaddleOCR](./awesome_projects.md)|
* 👫 Join the [PaddlePaddle Community](https://github.com/PaddlePaddle/community), where you can engage with [paddlepaddle developers](https://www.paddlepaddle.org.cn/developercommunity), researchers, and enthusiasts from around the world.
* 🎓 Learn from experts through workshops, tutorials, and Q&A sessions [hosted by the AI Studio](https://aistudio.baidu.com/learn/center).
* 🏆 Participate in [hackathons, challenges, and competitions](https://aistudio.baidu.com/competition) to showcase your skills and win exciting prizes.
* 📣 Stay updated with the latest news, announcements, and events by following our [Twitter](https://x.com/PaddlePaddle) and [WeChat](https://mp.weixin.qq.com/s/MAdo7fZ6dfeGcCQUtRP2ag).
