PaddleOCR is an Apache-2.0 Python OCR and document AI toolkit that converts PDFs and images into structured, LLM-ready JSON or Markdown across more than 100 languages, and it is built for developers who need to feed documents into RAG pipelines, agents, and document-parsing services.

What it is

PaddleOCR is an open-source OCR toolkit and document AI engine distributed on PyPI as the paddleocr package. It runs on Python 3.8 through 3.12 across Linux, Windows, and macOS, and it targets CPU, GPU, XPU, and NPU hardware. The project ships the PP-OCR text detection and recognition family and PP-Structure for document structure parsing, alongside PaddleOCR-VL-1.6, a 0.9B lightweight document vision-language model. It lives in the Python AI ecosystem and exposes its own homepage at paddleocr.com, with documentation mirrored in English, Simplified Chinese, Traditional Chinese, Japanese, Korean, French, Russian, Spanish, and Arabic.

The concrete problem it solves is that PDFs and scanned images carry text and layout that large language models cannot consume directly. PaddleOCR turns those raw documents into structured JSON and Markdown, which is the format RAG ingestion, retrieval, and agent workflows actually need. It replaces the plain-text extraction step that returns unstructured strings, and the topics the project carries describe exactly that work: pdf2markdown, pdf-parser, pdf-extractor-rag, document-parsing, kie for key information extraction, and document-translation.

Key capabilities

• Converts PDF and image documents into structured JSON or Markdown as LLM-ready output, matching the pdf2markdown and pdf-extractor-rag topics.
• Supports more than 100 languages, including dedicated Chinese OCR coverage through the chineseocr topic.
• Includes PaddleOCR-VL-1.6 (0.9B), a lightweight state-of-the-art document vision-language model.
• Provides the PP-OCR detection and recognition models plus PP-Structure for layout and structure parsing.
• Performs key information extraction (kie) to pull named fields out of documents rather than dumping raw text.
• Handles document translation via the document-translation topic.
• Runs on CPU, GPU, XPU, and NPU across Linux, Windows, and macOS on Python 3.8 to 3.12.

Who uses it and how

• RAG and agent platforms including Dify, RAGFlow, and Cherry Studio build on PaddleOCR as their document ingestion layer.
• More than 6,000 repositories depend on the project, so it functions as shared infrastructure rather than a single-application tool.
• Teams working with Chinese-language and mixed-script documents use it where CJK accuracy decides whether retrieval works at all.
• Scientific and research document workflows are covered by the ai4science topic.
• Projects needing a self-hosted parser deploy the paddleocr package on their own CPU or accelerator hardware instead of calling an external service.

Getting started

Install the paddleocr package from PyPI onto Python 3.8 to 3.12, or use the hosted offering at https://www.paddleocr.com if running the models in-house is not required. Documentation is also indexed on DeepWiki for the PaddlePaddle/PaddleOCR repository.

How it compares

The facts provided do not name a list of paid products that PaddleOCR replaces, and they name no directly comparable OCR or document-parsing tool. On the evidence available here, it stands alone in this registry.

When to use it — and when not to

PaddleOCR is a strong fit when documents must stay in-house on self-managed hardware, and the Apache-2.0 licence removes most legal friction for commercial embedding. A self-hoster must operate a Python 3.8 to 3.12 environment and choose accelerator hardware, since the project spans CPU, GPU, XPU, and NPU targets rather than shipping as a single managed service. Teams that want zero operational surface, or that only need a hosted API, should look elsewhere; note also that the README excerpt provided is truncated and does not contain an explicit install command, and the repository carries 243 open issues.