PaddlePaddle/PaddleOCR

PaddleOCR – Open‑source OCR & Document‑AI Toolkit

What it does

• Turns images or PDF pages into machine‑readable text, tables, formulas and even structured markdown/JSON that can be fed directly to large language models.
• Provides two main families of models:
  1. PP‑OCRv6 – a multilingual scene‑text recognizer (50 languages in a single model, >100 % speed‑up on CPU, high accuracy).
  2. PaddleOCR‑VL / PP‑StructureV3 – lightweight vision‑language models for full‑document parsing (text, tables, formulas, seals, charts) with outputs in JSON or Markdown.

Why it matters

• OCR is the first step for Retrieval‑Augmented Generation (RAG) pipelines: you need clean, structured text before an LLM can reason over a document.
• PaddleOCR claims state‑of‑the‑art accuracy on benchmarks like OmniDocBench while keeping the model size tiny (≈0.9 B parameters) and supporting a wide range of hardware (CPU, GPU, XPU, NPU).
• The project is heavily integrated with popular AI‑agent platforms such as Dify, RAGFlow and Cherry Studio, making it a convenient “data engine” for building LLM‑powered agents.

Key features (as described in the README)

• Multilingual support – 100+ languages, unified single model for Chinese, English, Japanese and 46 Latin‑script languages.
• Structure‑aware conversion – extracts coordinates for each text box, table cell, etc., enabling downstream layout‑aware processing.
• Fast inference – up to 5× speed‑up on CPU, 6× on Apple M4, and sub‑0.2 s on an A100 GPU for the medium‑size model.
• Deployment flexibility – works on Linux, Windows, macOS; supports Paddle static/dynamic graphs, Transformers, ONNX, OpenVINO, TensorRT, and even a browser SDK (PaddleOCR.js).
• One‑click cloud/edge – pre‑built Docker images, vLLM‑compatible serving for the new HPD‑Parsing model, and model mirrors on HuggingFace/ModelScope.
• Extensible pipeline – can convert PDFs, Word/Excel/PowerPoint files to Markdown, export results to DOCX, and includes utilities for chart‑to‑table conversion.

Typical usage flow

1. Install via pip install paddleocr (or pull a Docker image).
2. Run a ready‑made pipeline, e.g. paddleocr --image mydoc.png for quick OCR, or use the Python API to invoke PP-StructureV3 for full document parsing.
3. Consume the JSON/Markdown output in a downstream RAG pipeline, fine‑tune an LLM, or feed it to an AI agent.

Who uses it

• Over 6 k GitHub projects depend on PaddleOCR.
• Adopted by commercial RAG platforms (Dify, RAGFlow) and AI‑studio services.

Where to learn more

• Official website & demo: https://www.paddleocr.com
• Model cards on HuggingFace (e.g., PaddlePaddle/PaddleOCR-VL-1.6).
• Detailed docs for each pipeline are linked in the README (OCR, StructureV3, HPD‑Parsing, etc.).

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All statements are taken directly from the repository’s README; no additional claims have been added.