PaddleOCR converts PDF documents and images into structured, LLM-ready data formats including JSON and Markdown. It includes a lightweight 0.9B vision-language model alongside structure-aware conversion capabilities for downstream retrieval and processing tasks.
Project overview
The project bridges unstructured documents and language models by providing fine-grained coordinate extraction and structured outputs through a 0.9B lightweight vision-language model.
Project type
RAG · Document Processing
Use cases
Documents & Office · Knowledge Q&A
Deployment
Refer to project documentation
License
Apache-2.0
Best for
Developers and AI engineers building RAG pipelines or agentic applications who need to convert PDFs and images into structured JSON or Markdown.
Data teams requiring fine-grained coordinate information, including table cell data, from complex PDF documents and images.
Key capabilities
Features PaddleOCR-VL-1.6 (0.9B), a lightweight vision-language model for document parsing with structured outputs in Markdown and JSON.
Converts complex PDFs and images into Markdown or JSON, providing fine-grained coordinate information including table cell and text coordinates.
Converts common document formats such as Word, Excel, and PowerPoint into Markdown.
Exports parsed results to DOCX for convenient viewing and editing in Microsoft Word.
Official browser inference SDK that supports running PP-OCRv5 directly in the browser.
Converts models to ONNX format for deployment flexibility.
Accelerates inference using engines like OpenVINO, ONNX Runtime, and TensorRT.
Accelerates inference using multi-GPU and multi-process.
Limitations and risks
Full functionality is dependent on Paddle or supported inference backends.
An end-to-end local-only data boundary is not documented; external services may be accessed.
Telemetry tracking, cost dependencies, and minimum hardware specifications are not documented.
Specific models, including PaddleOCR-VL-1.6 and PP-OCRv6, are required for operation.
Getting started
Setup difficulty is rated medium because it requires a Python environment and following documentation for specific pipelines. Users should install via pip, refer to PP-OCR or PP-Structure documentation, and run the pipeline locally.
Alternatives and comparisons
Converts PDFs, images, and Office documents into Markdown and JSON for downstream retrieval and extraction tasks.
A scalable RAG microservice for processing, embedding, and querying document corpora with cited answers.
Manages personal knowledge bases and queries PDF documents accurately with retrieval-based refusal to avoid hallucination.