opendataloader-pdf: a high-accuracy PDF parser for AI-ready data extraction and automated PDF accessibility tagging

What it solves

OpenDataLoader PDF is a high-accuracy PDF parser designed to convert PDFs into structured data for AI and LLM pipelines (such as RAG), while also automating the creation of accessible, screen-reader-ready Tagged PDFs to reduce the high cost of manual accessibility remediation.

How it works

The tool provides two primary processing modes:

• Deterministic Local Mode: Uses fast Java-based processing for standard digital PDFs, extracting text, headings, and simple tables with high speed.
• Hybrid AI Mode: Routes complex pages (containing borderless tables, LaTeX formulas, or scanned images) to an AI backend for higher accuracy. This mode includes built-in OCR for 80+ languages and uses a lightweight vision model (SmolVLM) to generate descriptions for charts and images.

For accessibility, it performs layout analysis and auto-tagging to transform untagged PDFs into Tagged PDFs based on the Well-Tagged PDF specification.

Who it’s for

• AI Engineers: Those building RAG pipelines who need clean, structured Markdown or JSON with bounding boxes for source citations.
• Accessibility Specialists: Organizations needing to automate the conversion of untagged PDFs into accessible formats to comply with global regulations (e.g., ADA, EAA).
• Developers: Users requiring SDKs in Python, Node.js, or Java to integrate PDF parsing into their applications.

Highlights

• Benchmark Leader: Ranks #1 in overall extraction accuracy (0.907) across reading order, tables, and headings.
• Multi-Format Output: Exports to Markdown, JSON (with bounding boxes), HTML, and Tagged PDFs.
• AI-Enhanced Extraction: Supports LaTeX formula extraction and AI-generated descriptions for images and charts.
• AI Safety: Includes filters to protect against prompt injection attacks hidden in PDF layers.
• Open Source Core: The layout analysis and auto-tagging features are provided under the Apache 2.0 license.