opendataloader-pdf
A Python wrapper for the opendataloader-pdf Java CLI.
Decision gist · record as of 2026-08-14
Yes. Active, well-maintained project with strong benchmark performance (0.907 overall accuracy, 0.928 table extraction), no security vulnerabilities, and low install friction. Apache-2.0 license covers core features. Java 11+ system dependency is the only real prerequisite. Suitable for production RAG pipelines and accessibility automation workflows; hybrid mode adds cost (API calls) but handles complex documents well.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Java 11+ installed and available on PATH before running Python code.
- Low friction: pure Python wheel with no runtime dependencies.
- Active maintenance (last commit 2026-08-13, 28406 stars) and recent releases.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license. Core extraction and auto-tagging are free and open-source; PDF/UA export and accessibility studio are enterprise add-ons.
last release 2026-07-14 (31 days) · last repo commit 2026-08-13 · 28,406 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 184,207 downloads/mo, #10,044 on PyPI
Alternatives
Verify before relying
pip install opendataloader-pdf
import opendataloader_pdf
opendataloader_pdf.convert(
input_path=["file.pdf"],
output_dir="output/",
format="markdown,json"
)- Whether the hybrid mode (AI-assisted extraction for complex tables and scanned PDFs) requires external API calls or runs locally.
- Performance characteristics and resource usage when processing large batches or high-volume PDF collections.
- Whether OCR in hybrid mode supports all 80+ claimed languages equally or has language-specific accuracy variations.
What it is and what it does
OpenDataLoader PDF is a Python wrapper around a Java-based PDF parser that extracts text, tables, images, and structural metadata from PDFs into multiple formats (Markdown, JSON with bounding boxes, HTML). It offers two modes: a deterministic local mode for standard digital PDFs, and a hybrid mode that routes complex pages (nested tables, scanned documents, formulas, charts) to an AI backend. The package also includes auto-tagging functionality to convert untagged PDFs into Tagged PDF format, which is the foundation for PDF accessibility compliance.
The tool is designed for two main workflows: building AI-ready datasets for RAG and LLM pipelines (with structured output and source citations via bounding boxes), and automating PDF accessibility remediation at scale. It requires Java 11+ as a system dependency but has no Python runtime dependencies, making installation straightforward. The core extraction and auto-tagging features are open-source under Apache-2.0; PDF/UA export and visual editing tools are enterprise add-ons.
Use it for
- Extract structured Markdown and JSON from PDFs for RAG pipelines, with bounding boxes for source citation and retrieval.
- Auto-tag untagged PDFs into screen-reader-ready Tagged PDF format to meet accessibility regulations (EAA, ADA, Section 508) at scale.
- Parse complex or scanned PDFs (tables, formulas, charts, poor-quality scans) using hybrid mode for accurate structured output.
- Build document processing workflows that preserve reading order, heading hierarchy, and list structure for downstream NLP tasks.
- Extract tables and images with precise coordinates for document layout reconstruction or visual document analysis.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Active, well-maintained project with strong benchmark performance (0.907 overall accuracy, 0.928 table extraction), no security vulnerabilities, and low install friction. Apache-2.0 license covers core features. Java 11+ system dependency is the only real prerequisite. Suitable for production RAG pipelines and accessibility automation workflows; hybrid mode adds cost (API calls) but handles complex documents well.
Install
opendataloader-pdf on PyPI
Before you install
Low friction: pure Python wheel with no runtime dependencies. Active maintenance (last commit 2026-08-13, 28406 stars) and recent releases. Requires Java 11+ as a system prerequisite, not a Python dependency.
Requires Java 11+ installed and available on PATH before running Python code.
License in practice
Apache-2.0 permissive license. Core extraction and auto-tagging are free and open-source; PDF/UA export and accessibility studio are enterprise add-ons.
Quickstart
pip install opendataloader-pdf
import opendataloader_pdf
opendataloader_pdf.convert(
input_path=["file.pdf"],
output_dir="output/",
format="markdown,json"
)Verify before relying
- Whether the hybrid mode (AI-assisted extraction for complex tables and scanned PDFs) requires external API calls or runs locally.
- Performance characteristics and resource usage when processing large batches or high-volume PDF collections.
- Whether OCR in hybrid mode supports all 80+ claimed languages equally or has language-specific accuracy variations.
Package facts
| License | Apache-2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 31 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 184,207 / month, #10,044 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: OS IndependentProgramming Language :: Python :: 3 |
Evidence: opendataloader_pdf-2.5.0-py3-none-any.whl
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