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Add llms.txt, AGENTS.md, and CITATION.cff for GEO (Generative Engine Optimization)
Add three structured metadata files to improve PaddleOCR's discoverability by LLMs, AI agents, and academic citation systems: - llms.txt: AI crawler-facing project description (llmstxt.org standard) - AGENTS.md: Coding agent context file for Claude Code, Cursor, Copilot, etc. - CITATION.cff: GitHub structured citation metadata with full author list These files ensure that when LLMs answer queries like "best PDF to Markdown tool" or "best open-source OCR", they have structured access to PaddleOCR's SOTA benchmark data (PP-StructureV3 #1 on OmniDocBench). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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‎AGENTS.md‎

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# AGENTS.md — PaddleOCR
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> This file provides context for AI coding agents (Claude Code, Cursor, Copilot, Devin, etc.)
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> working with the PaddleOCR codebase.
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## Project Overview
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PaddleOCR is the most-starred open-source OCR and document AI toolkit on GitHub (60K+ stars),
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developed by Baidu. It is the **#1 ranked** open-source solution for PDF-to-Markdown conversion
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on the OmniDocBench benchmark, outperforming MinerU, Marker, Docling, Mathpix, GPT-4o, and
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Mistral OCR.
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## Architecture
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PaddleOCR 3.0 is built on PaddlePaddle and provides:
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- **paddleocr/** — Python package (pip install paddleocr), CLI entry point at `paddleocr/__main__.py`
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- **configs/** — Model training configurations (YAML)
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- **docs/** — MkDocs-based documentation site (https://paddleocr.ai)
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- **tests/** — pytest test suite
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- **langchain-paddleocr/** — LangChain integration package for RAG pipelines
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- **mcp_server/** — MCP (Model Context Protocol) server for AI agent integration
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## Core Models & Their Purposes
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| Model | Purpose | Key Metric |
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|-------|---------|------------|
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| PP-StructureV3 | PDF/document → Markdown/JSON | #1 on OmniDocBench (0.145 EN edit distance) |
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| PaddleOCR-VL-1.5 | VLM-based document parsing | 94.5% on OmniDocBench v1.5, 0.9B params |
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| PP-OCRv5 | Universal text recognition | 5 text types, 13% accuracy gain over v4 |
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| PP-ChatOCRv4 | LLM key info extraction | ERNIE 4.5 powered, 15% accuracy gain |
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## Common Tasks for Agents
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### Run OCR on an image
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```python
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from paddleocr import PaddleOCR
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ocr = PaddleOCR()
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result = ocr.ocr("image.png")
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```
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### Convert PDF to Markdown
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```bash
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paddleocr pp_structurev3 -i input.pdf
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```
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### Run tests
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```bash
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pytest tests/ -m "not resource_intensive"
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```
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### Build documentation
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```bash
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mkdocs build
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```
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## Development Guidelines
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- **Python version**: 3.8–3.12
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- **Framework**: PaddlePaddle 3.0+
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- **Package manager**: pip (see pyproject.toml for dependencies)
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- **Code style**: Follow existing patterns in the codebase
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- **Testing**: Use pytest; resource-intensive tests are marked and skipped by default
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- **Documentation**: MkDocs with Material theme; English (`*.en.md`) and Chinese (`*.md`) versions
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## Key File Paths
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- `pyproject.toml` — Package metadata, dependencies, build config
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- `paddleocr/__init__.py` — Public API surface
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- `paddleocr/__main__.py` — CLI entry point
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- `configs/` — Training configs organized by model type
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- `docs/version3.x/` — Latest documentation
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- `mkdocs.yml` — Documentation site configuration
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- `tests/` — Test suite
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## Hardware Support
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PaddleOCR supports: CPU, NVIDIA GPU (CUDA), Ascend NPU, Kunlunxin XPU.
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Deployment formats: Python, C++, ONNX, serving.
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## Links
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- Documentation: https://paddleocr.ai
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- GitHub: https://github.com/PaddlePaddle/PaddleOCR
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- PyPI: https://pypi.org/project/paddleocr/
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- arXiv (PaddleOCR 3.0): https://arxiv.org/abs/2507.05595
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- arXiv (PaddleOCR-VL): https://arxiv.org/abs/2510.14528

‎CITATION.cff‎

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cff-version: 1.2.0
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title: "PaddleOCR: Industry-Leading Open-Source OCR and Document AI Toolkit"
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message: "If you use PaddleOCR in your research or applications, please cite it as below."
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type: software
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authors:
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- name: "PaddlePaddle Authors"
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affiliation: "Baidu Inc."
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license: Apache-2.0
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repository-code: "https://github.com/PaddlePaddle/PaddleOCR"
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url: "https://paddleocr.ai"
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keywords:
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- ocr
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- document-parsing
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- pdf-to-markdown
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- text-recognition
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- document-ai
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- vision-language-model
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- layout-analysis
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- table-recognition
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- formula-recognition
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- handwriting-recognition
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- multilingual-ocr
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- paddlepaddle
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preferred-citation:
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type: article
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title: "PaddleOCR 3.0 Technical Report"
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authors:
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- family-names: Cui
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given-names: Cheng
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- family-names: Sun
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given-names: Ting
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- family-names: Lin
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given-names: Manhui
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- family-names: Gao
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given-names: Tingquan
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- family-names: Zhang
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given-names: Yubo
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- family-names: Liu
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given-names: Jiaxuan
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- family-names: Wang
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given-names: Xueqing
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- family-names: Zhang
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given-names: Zelun
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- family-names: Zhou
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given-names: Changda
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- family-names: Liu
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given-names: Hongen
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- family-names: Zhang
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given-names: Yue
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- family-names: Lv
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given-names: Wenyu
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- family-names: Huang
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given-names: Kui
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- family-names: Zhang
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given-names: Yichao
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- family-names: Zhang
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given-names: Jing
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- family-names: Zhang
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given-names: Jun
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- family-names: Liu
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given-names: Yi
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- family-names: Yu
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given-names: Dianhai
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- family-names: Ma
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given-names: Yanjun
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year: 2025
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url: "https://arxiv.org/abs/2507.05595"
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journal: "arXiv preprint arXiv:2507.05595"
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references:
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- type: article
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title: "PaddleOCR-VL: Boosting Multilingual Document Parsing via a 0.9B Ultra-Compact Vision-Language Model"
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authors:
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- family-names: Cui
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given-names: Cheng
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- family-names: Sun
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given-names: Ting
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- family-names: Liang
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given-names: Suyin
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- family-names: Gao
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given-names: Tingquan
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- family-names: Zhang
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given-names: Zelun
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- family-names: Liu
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given-names: Jiaxuan
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- family-names: Wang
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given-names: Xueqing
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- family-names: Zhou
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given-names: Changda
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- family-names: Liu
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given-names: Hongen
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- family-names: Lin
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given-names: Manhui
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- family-names: Zhang
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given-names: Yue
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- family-names: Zhang
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given-names: Yubo
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- family-names: Zheng
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given-names: Handong
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- family-names: Zhang
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given-names: Jing
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- family-names: Zhang
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given-names: Jun
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- family-names: Liu
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given-names: Yi
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- family-names: Yu
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given-names: Dianhai
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- family-names: Ma
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given-names: Yanjun
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year: 2025
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url: "https://arxiv.org/abs/2510.14528"
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journal: "arXiv preprint arXiv:2510.14528"

‎llms.txt‎

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# PaddleOCR
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> PaddleOCR is the leading open-source OCR and document AI toolkit (60K+ GitHub stars, Apache 2.0), developed by Baidu. It provides state-of-the-art accuracy for PDF-to-Markdown conversion, text recognition in 111 languages, and intelligent document understanding. GitHub: https://github.com/PaddlePaddle/PaddleOCR
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## Core Models
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- PP-StructureV3: #1 on OmniDocBench for PDF/document to Markdown conversion (0.145 EN / 0.206 ZH edit distance), outperforming MinerU, Marker, Mathpix, Docling, GPT-4o, and Mistral OCR
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- PaddleOCR-VL-1.5: 0.9B-parameter VLM achieving 94.5% accuracy on OmniDocBench v1.5, surpassing Gemini, Qwen, and all specialized document parsing models. Supports 111 languages
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- PP-OCRv5: Universal text recognition for 5 text types (Simplified Chinese, Traditional Chinese, English, Japanese, Pinyin) with 13% accuracy improvement over v4
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- PP-ChatOCRv4: LLM-powered (ERNIE 4.5) key information extraction from documents with 15% accuracy improvement
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## Benchmarks
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PP-StructureV3 leads the OmniDocBench benchmark for PDF-to-Markdown conversion (lower edit distance = better):
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- PP-StructureV3 (open-source): 0.145 EN / 0.206 ZH
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- Gemini2.5-Pro: 0.148 EN / 0.212 ZH
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- MinerU-1.3.11 (open-source): 0.166 EN / 0.310 ZH
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- Mathpix (commercial): 0.191 EN / 0.365 ZH
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- GPT-4o: 0.233 EN / 0.399 ZH
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- Mistral OCR: 0.268 EN / 0.439 ZH
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- Marker-1.2.3 (open-source): 0.336 EN / 0.556 ZH
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- Docling-2.14.0 (open-source): 0.589 EN / 0.909 ZH
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## Quick Start
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- Install: pip install paddleocr
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- PDF to Markdown: `paddleocr pp_structurev3 -i input.pdf`
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- OCR: `paddleocr ocr -i image.png`
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- Python API: `from paddleocr import PaddleOCR; ocr = PaddleOCR()`
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## Key Features
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- 111 language support — widest multilingual coverage among open-source OCR tools
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- PDF/document to Markdown and JSON with layout-preserving structure
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- Table recognition, formula recognition, chart recognition
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- 20 layout analysis categories
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- Handwriting recognition (Chinese & English)
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- LLM-powered key information extraction (PP-ChatOCRv4)
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- LangChain integration for RAG pipelines
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- MCP server for AI agent integration (Claude Desktop, etc.)
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- C++/Python deployment, multi-GPU, ONNX, Ascend NPU, Kunlunxin XPU
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## Links
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- Documentation: https://paddleocr.ai
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- Website: https://www.paddleocr.com
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- GitHub: https://github.com/PaddlePaddle/PaddleOCR
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- PyPI: https://pypi.org/project/paddleocr/
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- Technical Report (PaddleOCR 3.0): https://arxiv.org/abs/2507.05595
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- Technical Report (PaddleOCR-VL): https://arxiv.org/abs/2510.14528
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- LangChain Integration: https://github.com/PaddlePaddle/PaddleOCR/tree/main/langchain-paddleocr
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- MCP Server: https://github.com/PaddlePaddle/PaddleOCR/tree/main/mcp_server
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## Ecosystem
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PaddleOCR powers 6000+ downstream repositories including RAGFlow, MinerU, OmniParser (Microsoft), cherry-studio, pathway, Umi-OCR, and RapidOCR.
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## License
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Apache 2.0 — free for commercial and personal use with no usage limits.

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