OpenDataLoader PDF
PDF Parser for AI-ready data. Automate PDF accessibility. Open-source.
🔍 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 — Auto-tag untagged PDFs into screen-reader-ready Tagged PDFs at scale. First open-source tool to generate Tagged PDFs end-to-end.
- 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 (Apache 2.0). Untagged PDF in → Tagged PDF out. No proprietary SDK dependency (auto-tagging)
- 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)
Get Started in 30 Seconds
Requires: Java 11+ and Python 3.11+ (Node.js | Java also available)
Before you start: run java -version. If not found, install JDK 11+ from Adoptium.
pip install -U opendataloader-pdf
import opendataloader_pdf
# Batch all files in one call — each convert() spawns a JVM process, so repeated calls are slow
opendataloader_pdf.convert(
input_path=["file1.pdf", "file2.pdf", "folder/"],
output_dir="output/",
format="markdown,json"
)
Annotated PDF output — each element (heading, paragraph, table, image) detected with bounding boxes and semantic type.
What Problems Does This Solve?
| Problem | Solution | Status |
|---|
| PDF structure lost during parsing — wrong reading order, broken tables, no element coordinates | Deterministic local PDF to Markdown/JSON with bounding boxes, XY-Cut++ reading order | Shipped |
| Complex tables, scanned PDFs, formulas, charts need AI-level understanding | Hybrid mode routes complex pages to AI backend (#1 in benchmarks) | Shipped |
| Manual PDF remediation cost — Accessibility regulations (EAA, ADA, Section 508) demand Tagged PDFs. Manual remediation costs $50–200/doc | Auto-tag untagged PDFs into Tagged PDFs (free, Apache 2.0). Foundation for PDF/UA workflows; full PDF/UA-1/2 export is an enterprise add-on | Auto-tag: Shipped. PDF/UA export: Enterprise |
This is the beginning of the README. Read the rest at the original. Read the full README ↗
