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opendataloader-project/opendataloader-pdf

📈 Trending★ 20,479JavaAudience · developerComplexity · 3/5ActiveLicenseSetup · moderate

TLDR

Java library for extracting structured data from PDFs and converting them into accessible, screen-reader-compatible formats. Outputs Markdown, JSON, and HTML with high accuracy for AI pipelines and accessibility compliance.

Mindmap

mindmap
  root((repo))
    Data Extraction
      Markdown output
      JSON with bounding boxes
      HTML format
    Accessibility
      Auto-tag PDFs
      Screen reader compatible
      PDF/UA compliance
    AI Features
      OCR in 80+ languages
      Table extraction
      Chart descriptions
    Performance
      0.907 accuracy score
      0.015 seconds per page
      Hybrid AI routing
    SDKs Available
      Python
      Node.js
      Java

Things people build with this

USE CASE 1

Extract structured data from PDFs to feed into RAG systems and large language models for AI applications.

USE CASE 2

Convert untagged PDFs into accessible Tagged PDFs that work with screen readers for regulatory compliance.

USE CASE 3

Process scanned PDFs with OCR to extract text, tables, and formulas from image-based documents.

USE CASE 4

Generate JSON with precise bounding box coordinates for every element to build custom PDF processing workflows.

Tech stack

JavaPython SDKNode.js SDKOCRXY-Cut++ algorithm

Getting it running

Difficulty · moderate Time to first run · 30min

OCR engine dependency and Java runtime setup required; SDK language choice affects initial configuration.

Use freely for any purpose, including commercial use, as long as you keep the copyright notice and include a copy of the license.

In plain English

OpenDataLoader PDF is an open-source tool that turns PDF files into clean, structured data that AI systems and accessibility software can use. PDFs are notoriously hard to read programmatically, text runs in the wrong order, tables collapse into mush, pictures lose their position. This project fixes that, and tackles a second problem: making untagged PDFs accessible to screen readers. For data extraction, you give it a PDF and it produces Markdown, JSON with bounding boxes for every element, or HTML. A deterministic local mode runs fast on your own machine; a hybrid mode routes complex pages (scanned PDFs needing OCR in 80+ languages, complex or borderless tables, LaTeX formulas, charts and image descriptions) to an AI backend. The README reports it ranks #1 overall with 0.907 accuracy and 0.928 table accuracy on a 200-PDF benchmark. It includes prompt-injection filtering, header/footer/watermark filtering, and an XY-Cut++ algorithm for correct reading order. For accessibility, it auto-tags untagged PDFs into Tagged PDFs, the foundation for screen-reader compatibility and PDF/UA compliance. You would use this if you are building a Retrieval-Augmented Generation (RAG) pipeline that needs structured content from PDFs, or to make a library of PDFs accessible under regulations like the EAA, ADA, or Section 508 without paying $50, 200 per document for manual remediation. The core is written in Java (requires Java 11+) with SDKs for Python, Node.js, and Java, plus a LangChain integration. Auto-tagging and basic extraction are Apache 2.0; PDF/UA export and a visual accessibility studio are enterprise add-ons. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
Show me how to use OpenDataLoader PDF to extract table data from a PDF and output it as JSON with bounding boxes.
Prompt 2
How do I set up OpenDataLoader PDF in Python to convert a batch of untagged PDFs into accessible Tagged PDFs?
Prompt 3
What's the hybrid mode in OpenDataLoader PDF and when should I use it instead of local-only extraction?
Prompt 4
How can I integrate OpenDataLoader PDF into a RAG pipeline to prepare PDFs for a language model?
Prompt 5
Show me example code for extracting Markdown from a scanned PDF using OpenDataLoader PDF's OCR feature.
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Generated 2026-05-21 · Model: sonnet-4-6 · Verify against the repo before relying on details.