PaddleOCR converts PDF documents and images into structured, LLM-ready data (JSON/Markdown) with industry-leading accuracy. With 70k+ Stars and trusted by top-tier projects like Dify, RAGFlow, and Cherry Studio, PaddleOCR is the bedrock for building intelligent RAG and Agentic applications.

🚀 Key Features

📄 Intelligent Document Parsing (LLM-Ready)

Transforming messy visuals into structured data for the LLM era.

• SOTA Document VLM: Featuring PaddleOCR-VL-1.5 (0.9B), the industry's leading lightweight vision-language model for document parsing. It excels in parsing complex documents across 5 major "Real-World" challenges: Warping, Scanning, Screen Photography, Illumination, and Skewed documents, with structured outputs in Markdown and JSON formats.
• Structure-Aware Conversion: Powered by PP-StructureV3, seamlessly convert complex PDFs and images into Markdown or JSON. Unlike the PaddleOCR-VL series models, it provides more fine-grained coordinate information, including table cell coordinates, text coordinates, and more.
• Production-Ready Efficiency: Achieve commercial-grade accuracy with an ultra-small footprint. Outperforms numerous closed-source solutions in public benchmarks while remaining resource-efficient for edge/cloud deployment.

🔍 Universal Text Recognition (Scene OCR)

The global gold standard for high-speed, multilingual text spotting.

• 100+ Languages Supported: Native recognition for a vast global library. Our PP-OCRv5 single-model solution elegantly handles multilingual mixed documents (Chinese, English, Japanese, Pinyin, etc.).
• Complex Element Mastery: Beyond standard text recognition, we support natural scene text spotting across a wide range of environments, including IDs, street views, books, and industrial components
• Performance Leap: PP-OCRv5 delivers a 13% accuracy boost over previous versions, maintaining the "Extreme Efficiency" that PaddleOCR is famous for.

🛠️ Developer-Centric Ecosystem

• Seamless Integration: The premier choice for the AI Agent ecosystem—deeply integrated with Dify, RAGFlow, Pathway, and Cherry Studio.
• LLM Data Flywheel: A complete pipeline to build high-quality datasets, providing a sustainable "Data Engine" for fine-tuning Large Language Models.
• One-Click Deployment: Supports various hardware backends (NVIDIA GPU, Intel CPU, Kunlunxin XPU, and diverse AI Accelerators).

📣 Recent updates

🔥 [2026.01.29] PaddleOCR v3.4.0 Released: The Era of Irregular Document Parsing

• PaddleOCR-VL-1.5 (SOTA 0.9B VLM): Our latest flagship model for document parsing is now live!
  • 94.5% Accuracy on OmniDocBench: Surpassing top-tier general large models and specialized document parsers.
  • Real-World Robustness: First to introduce the PP-DocLayoutV3 algorithm for irregular shape positioning, mastering 5 tough scenarios: Skew, Warping, Scanning, Illumination, and Screen Photography.
  • Capability Expansion: Now supports Seal Recognition, Text Spotting, and expands to 111 languages (including China’s Tibetan script and Bengali).
  • Long Document Mastery: Supports automatic cross-page table merging and hierarchical heading identification.
  • Try it now: Available on HuggingFace or our Official Website.

History Log

🚀 Quick Start

Step 1: Try Online

PaddleOCR official website provides interactive Experience Center and APIs—no setup required, just one click to experience.

👉 Visit Official Website

Step 2: Local Deployment

For local usage, please refer to the following documentation based on your needs:

• PP-OCR Series: See PP-OCR Documentation
• PaddleOCR-VL Series: See PaddleOCR-VL Documentation
• PP-StructureV3: See PP-StructureV3 Documentation
• More Capabilities: See More Capabilities Documentation

🧩 More Features

• Convert models to ONNX format: Obtaining ONNX Models.
• Accelerate inference using engines like OpenVINO, ONNX Runtime, TensorRT, or perform inference using ONNX format models: High-Performance Inference.
• Accelerate inference using multi-GPU and multi-process: Parallel Inference for Pipelines.
• Integrate PaddleOCR into applications written in C++, C#, Java, etc.: Serving.

🔄 Quick Overview of Execution Results

PP-OCRv5

PP-StructureV3

PaddleOCR-VL

✨ Stay Tuned

⭐ Star this repository to keep up with exciting updates and new releases, including powerful OCR and document parsing capabilities! ⭐

👩‍👩‍👧‍👦 Community

PaddlePaddle WeChat official account	Join the tech discussion group

😃 Awesome Projects Leveraging PaddleOCR

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
Dify	Production-ready platform for agentic workflow development.
RAGFlow	RAG engine based on deep document understanding.
pathway	Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
MinerU	Multi-type Document to Markdown Conversion Tool
Umi-OCR	Free, Open-source, Batch Offline OCR Software.
cherry-studio	A desktop client that supports for multiple LLM providers.
haystack	AI orchestration framework to build customizable, production-ready LLM applications.
OmniParser	OmniParser: Screen Parsing tool for Pure Vision Based GUI Agent.
QAnything	Question and Answer based on Anything.
Learn more projects	More projects based on PaddleOCR

👩‍👩‍👧‍👦 Contributors

🌟 Star

📄 License

This project is released under the Apache 2.0 license.

🎓 Citation

@misc{cui2025paddleocr30technicalreport,
      title={PaddleOCR 3.0 Technical Report}, 
      author={Cheng Cui and Ting Sun and Manhui Lin and Tingquan Gao and Yubo Zhang and Jiaxuan Liu and Xueqing Wang and Zelun Zhang and Changda Zhou and Hongen Liu and Yue Zhang and Wenyu Lv and Kui Huang and Yichao Zhang and Jing Zhang and Jun Zhang and Yi Liu and Dianhai Yu and Yanjun Ma},
      year={2025},
      eprint={2507.05595},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2507.05595}, 
}

@misc{cui2025paddleocrvlboostingmultilingualdocument,
      title={PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model}, 
      author={Cheng Cui and Ting Sun and Suyin Liang and Tingquan Gao and Zelun Zhang and Jiaxuan Liu and Xueqing Wang and Changda Zhou and Hongen Liu and Manhui Lin and Yue Zhang and Yubo Zhang and Handong Zheng and Jing Zhang and Jun Zhang and Yi Liu and Dianhai Yu and Yanjun Ma},
      year={2025},
      eprint={2510.14528},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2510.14528}, 
}

@misc{cui2026paddleocrvl15multitask09bvlm,
      title={PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing}, 
      author={Cheng Cui and Ting Sun and Suyin Liang and Tingquan Gao and Zelun Zhang and Jiaxuan Liu and Xueqing Wang and Changda Zhou and Hongen Liu and Manhui Lin and Yue Zhang and Yubo Zhang and Yi Liu and Dianhai Yu and Yanjun Ma},
      year={2026},
      eprint={2601.21957},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2601.21957}, 
}