All Repos
RAG & RetrievalPythonApache-2.0

PaddleOCR

PaddlePaddle/PaddleOCR · Updated

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.

PaddlePaddle/PaddleOCR is a Python repository in the RAG & Retrieval category with 91,932 stars and 11,583 forks on GitHub, released under the Apache-2.0 license, last updated 1mo ago.

#ai4science#chineseocr#document-parsing#document-translation#kie#ocr#paddleocr-vl#pdf-extractor-rag
View on GitHub 92kGitHub stars

How to install PaddleOCR in your AI client

Setup for Claude Code, Claude Desktop, Cursor, Codex, Windsurf, Cline and VS Code — copy-paste ready.

How to install PaddleOCR in Claude CodeAnthropic's agentic CLI

  1. Clone & open

    bash
    git clone https://github.com/PaddlePaddle/PaddleOCR.git
cd PaddleOCR
claude

  2. Let Claude read it

    Ask “/init” to generate a CLAUDE.md, then have Claude explain, run, or extend the codebase. It reads files on demand.

Documentation

Full README

Global Leading OCR Toolkit & Document AI Engine

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

Excerpt — the full document is on GitHub.

More RAG & Retrieval repositories

Catalog data refreshed . Something wrong? Report it.