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paddlepaddle/paddleocr

🔥 Hot78,075PythonAudience · developerComplexity · 3/5ActiveLicenseSetup · moderate

TLDR

Open-source toolkit that reads text from images, PDFs, and documents, then structures the output so AI systems can process it.

Mindmap

mindmap
  root((PaddleOCR))
    What it does
      Reads text from images
      Structures documents
      Detects tables and charts
      Supports 100+ languages
    How it works
      Document parsing pipeline
      Vision-language model
      Handles skewed photos
      Outputs Markdown or JSON
    Use cases
      Extract from invoices
      Build document search
      Process PDFs for AI
      Automate form data
    Tech stack
      Python
      PaddlePaddle framework
      CPU and GPU support
      Cross-platform

Things people build with this

USE CASE 1

Extract text and tables from invoices, receipts, and financial documents at scale.

USE CASE 2

Build a document search engine that indexes PDFs and scanned images for keyword lookup.

USE CASE 3

Automate data extraction from forms, ID cards, and structured documents.

USE CASE 4

Create RAG pipelines that retrieve and process document archives for AI systems.

Tech stack

PythonPaddlePaddleNVIDIA GPUCPULinuxWindowsmacOS

Getting it running

Difficulty · moderate Time to first run · 30min

PaddlePaddle and model downloads required; GPU optional but CPU inference is slow.

Open-source toolkit available under a permissive license allowing free use for research and commercial applications.

In plain English

PaddleOCR is an open-source OCR (Optical Character Recognition) toolkit developed by Baidu's PaddlePaddle AI platform. OCR is the technology that reads text from images, scanned documents, and PDFs, converting visual text into machine-readable data. The problem PaddleOCR addresses is that many real-world documents (invoices, ID cards, books, street signs, handwritten notes) exist as images or PDFs, not as structured text, making them inaccessible to software that needs to process or search the content. The toolkit does more than just read individual characters. Its document parsing pipeline, called PP-StructureV3, can analyze a full page: detect text blocks, tables, charts, figures, and headers, then output the entire document as structured Markdown or JSON, formats that AI systems like LLMs (large language models) can directly consume. A vision-language model called PaddleOCR-VL-1.5 handles complex real-world documents that are skewed, poorly lit, warped, or photographed from a screen rather than scanned cleanly. The system supports over 100 languages including Chinese, Japanese, Arabic, and mixed multilingual documents. It's designed for both research and production: it can run on CPUs, NVIDIA GPUs, and specialized AI accelerators, and has been integrated into popular AI frameworks like Dify and RAGFlow (tools for building AI pipelines with document retrieval). You would use PaddleOCR when you need to extract text from documents at scale, process PDFs for AI systems, build a document search engine, automate data extraction from forms, or create RAG (Retrieval-Augmented Generation) pipelines that need to search through document archives. The tech stack is Python, built on the PaddlePaddle deep learning framework. It runs on Linux, Windows, and macOS, supports multiple hardware backends, and is installed via pip.

Copy-paste prompts

Prompt 1
How do I use PaddleOCR to extract text from a batch of PDF files and output the results as JSON?
Prompt 2
Show me how to set up PaddleOCR to detect tables and convert them to structured data in a document.
Prompt 3
I need to process multilingual documents with PaddleOCR. How do I configure it for mixed Chinese and English text?
Prompt 4
How can I integrate PaddleOCR into a RAG pipeline to index and search through document archives?
Prompt 5
What's the best way to handle skewed or poorly lit photos of documents with PaddleOCR?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.