개요
PaddleOCR은 강력한 OCR 툴킷으로, 모든 PDF 또는 이미지를 LLM에서 사용 가능한 구조화된 데이터(JSON/Markdown)로 변환합니다. 100개 이상의 언어를 지원하며, PP-StructureV3 및 PaddleOCR-VL 모델의 최첨단 문서 분석 기능을 갖추고 있으며, RAG 및 스마트 에이전트 애플리케이션과 원활하게 통합됩니다.
README 미리보기
\n\n \n \n \n\n\n\nGlobal Leading OCR Toolkit & Document AI Engine\n\nEnglish | [简体中文](./readme/README_cn.md) | [繁體中文](./readme/README_tcn.md) | [日本語](./readme/README_ja.md) | [한국어](./readme/README_ko.md) | [Français](./readme/README_fr.md) | [Русский](./readme/README_ru.md) | [Español](./readme/README_es.md) | [العربية](./readme/README_ar.md)\n\n\n\n[](https://pepy.tech/projects/paddleocr)\n[](https://github.com/PaddlePaddle/PaddleOCR/network/dependents)\n\n\n\n\n[](https://www.paddleocr.com)\n[](https://deepwiki.com/PaddlePaddle/PaddleOCR)\n[](../LICENSE)\n\n\n\n\n\n\n\n\n**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.**\n\n\n## 🚀 Key Features\n\n### 📄 Intelligent Document Parsing (LLM-Ready)\n> *Transforming messy visuals into structured data for the LLM era.*\n\n* **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.\n* **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.\n* **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.\n\n### 🔍 Universal Text Recognition (S