paddleocr-text-recognition

English | 简体中文 | 繁體中文 | 日本語 | 한국어 | Français | Русский | Español | العربية

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-1.6 (0.9B), the industry's leading lightweight vision-language model for document parsing. It achieves 96.3% accuracy on OmniDocBench v1.6, leads in text, formula, and table recognition, and shows significantly enhanced capabilities in ancient documents, rare characters, seals, and charts, 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. PP-OCRv6 supports 50 languages with a single unified model (Chinese, English, Japanese, and 46 Latin-script languages) — no model switching needed for multilingual documents.
• 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-OCRv6 achieves +4.6% detection and +5.1% recognition accuracy over PP-OCRv5, surpassing mainstream Vision-Language Models. 5.2× CPU inference speedup end-to-end.

🛠️ 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

🔥 2026.07.22: HPD-Parsing is now available

• HPD-Parsing is a lightweight vision-language model designed for high-throughput document parsing. It adopts a hierarchical parallel decoding paradigm and Progressive Multi-Token Prediction (P-MTP), achieving a peak throughput of 4,752 tokens/s on public benchmarks while maintaining competitive parsing accuracy.
• HPD-Parsing supports both OpenAI-compatible serving and local inference through a customized vLLM runtime, making it suitable for document parsing scenarios with high demands on inference efficiency and deployment throughput.
• See the HPD-Parsing usage tutorial for environment setup, serving, and local inference instructions.

• PP-OCRv6 highlights:

  • Accuracy boost: Medium tier achieves +4.6% detection and +5.1% recognition over PP-OCRv5_server, surpassing mainstream VLMs (Qwen3-VL-235B, GPT-5.5) with only 34.5M parameters.
  • 50 languages unified: Single model covers Chinese, English, Japanese, and 46 Latin-script languages — no model switching needed.
  • Specialized scenarios: Major improvemen