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🔍 PDF parser for AI data extraction — Extract Markdown, JSON (with bounding boxes), and HTML from any PDF. #1 in benchmarks (0.907 overall). Deterministic local mode + AI hybrid mode for complex pages.
- How accurate is it? — #1 in benchmarks: 0.907 overall, 0.928 table accuracy across 200 real-world PDFs including multi-column and scientific papers. Deterministic local mode + AI hybrid mode for complex pages (benchmarks)
- Scanned PDFs and OCR? — Yes. Built-in OCR (80+ languages) in hybrid mode. Works with poor-quality scans at 300 DPI+ (hybrid mode)
- Tables, formulas, images, charts? — Yes. Complex/borderless tables, LaTeX formulas, and AI-generated picture/chart descriptions all via hybrid mode (hybrid mode)
- How do I use this for RAG? —
pip install opendataloader-pdf, convert in 3 lines. Outputs structured Markdown for chunking, JSON with bounding boxes for source citations, and HTML. LangChain integration available. Python, Node.js, Java SDKs (quick start | LangChain)
♿ PDF accessibility automation — The same layout analysis engine also powers auto-tagging. First open-source tool to generate Tagged PDFs end-to-end (coming Q2 2026).
- What's the problem? — Accessibility regulations are now enforced worldwide. Manual PDF remediation costs $50–200 per document and doesn't scale (regulations)
- What's free? — Layout analysis + auto-tagging (Q2 2026, Apache 2.0). Untagged PDF in → Tagged PDF out. No proprietary SDK dependency (auto-tagging preview)
- What about PDF/UA compliance? — Converting Tagged PDF to PDF/UA-1 or PDF/UA-2 is an enterprise add-on. Auto-tagging generates the Tagged PDF; PDF/UA export is the final step (pipeline)
- Why trust this? — Built in collaboration with Dual Lab (veraPDF developers) based on PDF Association specifications, best practice guides and expertise of the PDF Community. Auto-tagging follows the Well-Tagged PDF specification, validated with veraPDF (collaboration)
Tags:
tool
ai
document
markdown
Last modified 15 April 2026