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)

Requires: Java 11+ and Python 3.10+ (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.

opendataloader-pdf [hybrid] ranks #1 overall (0.907) across reading order, table, and heading extraction accuracy.

Scores normalized to [0, 1]. Higher is better for accuracy; lower is better for speed. Bold = best. Full benchmark details

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"
)

npm install @opendataloader/pdf

import { convert } from '@opendataloader/pdf';

await convert(['file1.pdf', 'file2.pdf', 'folder/'], {
  outputDir: 'output/',
  format: 'markdown,json'
});

<dependency>
  <groupId>org.opendataloader</groupId>
  <artifactId>opendataloader-pdf-core</artifactId>
</dependency>

Python Quick Start | Node.js Quick Start | Java Quick Start

Hybrid mode combines fast local Java processing with AI backends. Simple pages stay local (0.02s); complex pages route to AI for +90% table accuracy.

Don't combine with --use-struct-tree on tagged PDFs. --use-struct-tree takes precedence, so the hybrid backend is not called (a warning is logged). If you want the hybrid backend, drop --use-struct-tree.

pip install -U "opendataloader-pdf[hybrid]"

Terminal 1 — Start the backend server:

opendataloader-pdf-hybrid --port 5002

Terminal 2 — Process PDFs:

# Batch all files in one call — each invocation spawns a JVM process, so repeated calls are slow
opendataloader-pdf --hybrid docling-fast file1.pdf file2.pdf folder/

Python:

# 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/",
    hybrid="docling-fast"
)

Start the backend with --force-ocr for image-based PDFs with no selectable text:

opendataloader-pdf-hybrid --port 5002 --force-ocr

For non-English documents, specify the language:

opendataloader-pdf-hybrid --port 5002 --force-ocr --ocr-lang "ko,en"

Supported languages: en, ko, ja, ch_sim, ch_tra, de, fr, ar, and more.

Extract mathematical formulas as LaTeX from scientific PDFs:

# Server: enable formula enrichment
opendataloader-pdf-hybrid --enrich-formula

# Batch all files in one call — each invocation spawns a JVM process, so repeated calls are slow
opendataloader-pdf --hybrid docling-fast --hybrid-mode full file1.pdf file2.pdf folder/

Output in JSON:

{
  "type": "formula",
  "page number": 1,
  "bounding box": [226.2, 144.7, 377.1, 168.7],
  "content": "\\frac{f(x+h) - f(x)}{h}"
}

Note: Formula and picture description enrichments require --hybrid-mode full on the client side.

Generate AI descriptions for charts and images — useful for RAG search and accessibility alt text:

# Server
opendataloader-pdf-hybrid --enrich-picture-description

# Batch all files in one call — each invocation spawns a JVM process, so repeated calls are slow
opendataloader-pdf --hybrid docling-fast --hybrid-mode full file1.pdf file2.pdf folder/

Output in JSON:

{
  "type": "picture",
  "page number": 1,
  "bounding box": [72.0, 400.0, 540.0, 650.0],
  "description": "A bar chart showing waste generation by region from 2016 to 2030..."
}

Uses SmolVLM (256M), a lightweight vision model. Custom prompts supported via --picture-description-prompt.

Enterprise-grade AI document analysis via Hancom Data Loader — customer-customized models trained on your domain-specific documents. 30+ element types (tables, charts, formulas, captions, footnotes, etc.), VLM-based image/chart understanding, complex table extraction (merged cells, nested tables), SLA-backed OCR for scanned documents, and native HWP/HWPX support. Supports PDF, DOCX, XLSX, PPTX, HWP, PNG, JPG. Live demo

Hybrid Mode Guide

Combine formats: format="json,markdown"

{
  "type": "heading",
  "id": 42,
  "level": "Title",
  "page number": 1,
  "bounding box": [72.0, 700.0, 540.0, 730.0],
  "heading level": 1,
  "font": "Helvetica-Bold",
  "font size": 24.0,
  "text color": "[0.0]",
  "content": "Introduction"
}

Full JSON Schema

When a PDF has structure tags, OpenDataLoader extracts the exact layout the author intended — no guessing, no heuristics. Headings, lists, tables, and reading order are preserved from the source.

Output quality depends on tag quality. Not all tagged PDFs are well-tagged. For PDFs with sparse or incorrect tags, the default heuristic mode or --hybrid docling-fast often produces better results.

--use-struct-tree takes precedence over --hybrid. If both are set on a tagged PDF, the structure tree is used and the hybrid backend is not called (a well-tagged PDF already carries reading order and structure). Drop --use-struct-tree if you want the hybrid backend instead.

# 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/",
    use_struct_tree=True           # Use native PDF structure tags
)

Most PDF parsers ignore structure tags entirely. Learn more

PDFs can contain hidden prompt injection attacks. OpenDataLoader automatically filters:

• Hidden text (transparent, zero-size fonts)
• Off-page content
• Suspicious invisible layers

To sanitize sensitive data (emails, URLs, phone numbers → placeholders), enable it explicitly:

# Batch all files in one call — each invocation spawns a JVM process, so repeated calls are slow
opendataloader-pdf file1.pdf file2.pdf folder/ --sanitize

AI Safety Guide

pip install -U langchain-opendataloader-pdf

from langchain_opendataloader_pdf import OpenDataLoaderPDFLoader

loader = OpenDataLoaderPDFLoader(
    file_path=["file1.pdf", "file2.pdf", "folder/"],
    format="text"
)
documents = loader.load()

LangChain Docs | GitHub | PyPI

# 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="json,markdown,pdf",
    image_output="embedded",        # "off", "embedded" (Base64), or "external" (default)
    image_format="jpeg",            # "png" or "jpeg"
    use_struct_tree=True,           # Use native PDF structure
)

Full CLI Options Reference

Problem: Millions of existing PDFs lack structure tags, failing accessibility regulations (EAA, ADA/Section 508, Korea Digital Inclusion Act). Manual remediation costs $50–200 per document and doesn't scale.

OpenDataLoader's approach: Built in collaboration with PDF Association and Dual Lab (developers of veraPDF, the industry-reference open-source PDF/A and PDF/UA validator). Auto-tagging follows the Well-Tagged PDF specification and is validated programmatically using veraPDF — automated conformance checks against PDF accessibility standards, not manual review. No existing open-source tool generates Tagged PDFs end-to-end — most rely on proprietary SDKs for the tag-writing step. OpenDataLoader does it all under Apache 2.0. (collaboration details)

💼 Enterprise features are available on request. Contact us to get started.

Generate Tagged PDFs from untagged PDFs — output is a screen-reader-ready PDF with structure tags (headings, paragraphs, lists, tables, reading order).

import opendataloader_pdf

# Untagged PDF in → Tagged PDF out
opendataloader_pdf.convert(
    input_path=["file1.pdf", "file2.pdf", "folder/"],
    output_dir="output/",
    format="tagged-pdf"
)

# CLI
opendataloader-pdf --format tagged-pdf file1.pdf file2.pdf folder/

Combine with other formats: format="json,tagged-pdf".

Existing PDFs (untagged)
    │
    ▼
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐    ┌──────────────────┐
│  1. Audit       │───>│  2. Auto-Tag     │───>│  3. Export      │───>│  4. Studio       │
│  (check tags)   │    │  (→ Tagged PDF)  │    │  (PDF/UA)       │    │  (visual editor) │
└─────────────────┘    └──────────────────┘    └─────────────────┘    └──────────────────┘
        │                       │                       │                      │
        ▼                       ▼                       ▼                      ▼
  use_struct_tree      format="tagged-pdf"        PDF/UA export       Accessibility Studio
  (Available now)      (Available, Apache 2.0)    (Enterprise)        (Enterprise)

PDF Accessibility Guide

Full Roadmap

For RAG pipelines, you need a parser that preserves document structure, maintains correct reading order, and provides element coordinates for citations. OpenDataLoader is designed specifically for this — it outputs structured JSON with bounding boxes, handles multi-column layouts with XY-Cut++, and runs locally without GPU. In hybrid mode, it ranks #1 overall (0.907) in benchmarks.

OpenDataLoader PDF is the only open-source parser that combines: rule-based deterministic extraction (no GPU), bounding boxes for every element, XY-Cut++ reading order, built-in AI safety filters, native Tagged PDF support, and hybrid AI mode for complex documents. It ranks #1 in overall accuracy (0.907) while running locally on CPU.

OpenDataLoader detects tables using border analysis and text clustering, preserving row/column structure. For complex tables, enable hybrid mode for +90% accuracy improvement (0.489 to 0.928 TEDS score):

# 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="json",
    hybrid="docling-fast"           # For complex tables
)

OpenDataLoader [hybrid] ranks #1 overall (0.907) across reading order, table, and heading accuracy. Key differences: docling (0.882) is strong but lacks bounding boxes and AI safety filters. marker (0.861) requires GPU and is 1000x slower (53.932s/page). pymupdf4llm (0.732) is fast but has poor table (0.401) and heading (0.412) accuracy. OpenDataLoader is the only parser that combines deterministic local extraction, bounding boxes for every element, and built-in prompt injection protection. See full benchmark.

Yes. OpenDataLoader runs 100% locally. No API calls, no data transmission — your documents never leave your environment. The hybrid mode backend also runs locally on your machine. Ideal for legal, healthcare, and financial documents.

Yes, via hybrid mode. Install with pip install "opendataloader-pdf[hybrid]", start the backend with --force-ocr, then process as usual. Supports multiple languages including Korean, Japanese, Chinese, Arabic, and more via --ocr-lang.

Yes. For digital PDFs, text extraction works out of the box. For scanned PDFs, use hybrid mode with --force-ocr --ocr-lang "ko,en" (or ja, ch_sim, ch_tra). Coming soon: Hancom Data Loader integration — enterprise-grade AI document analysis with built-in production-grade OCR and customer-customized models optimized for your specific document types and workflows.

Local mode processes 60+ pages per second on CPU (0.02s/page). Hybrid mode processes 2+ pages per second (0.46s/page) with significantly higher accuracy for complex documents. No GPU required. Benchmarked on Apple M4. Full benchmark details. With multi-process batch processing, throughput exceeds 100 pages per second on 8+ core machines.

Yes. OpenDataLoader uses XY-Cut++ reading order analysis to correctly sequence text across multi-column pages, sidebars, and mixed layouts. This works in both local and hybrid modes without any configuration.

Hybrid mode combines fast local Java processing with an AI backend. Simple pages are processed locally (0.02s/page); complex pages (tables, scanned content, formulas, charts) are automatically routed to the AI backend for higher accuracy. The backend runs locally on your machine — no cloud required. See Which Mode Should I Use? and Hybrid Mode Guide.

Yes. Install langchain-opendataloader-pdf for an official LangChain document loader integration. See LangChain docs.

OpenDataLoader outputs structured Markdown with headings, tables, and lists preserved — ideal input for semantic chunking. Each element in JSON output includes type, heading level, and page number, so you can split by section or page boundary. For most RAG pipelines: parse with format="markdown" for text chunks, or format="json" when you need element-level control. Pair with LangChain's RecursiveCharacterTextSplitter or your own heading-based splitter for best results.

Every element in JSON output includes a bounding box ([left, bottom, right, top] in PDF points) and page number. When your RAG pipeline returns an answer, map the source chunk back to its bounding box to highlight the exact location in the original PDF. This enables "click to source" UX — users see which paragraph, table, or figure the answer came from. No other open-source parser provides bounding boxes for every element by default.

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"
)

OpenDataLoader preserves heading hierarchy, table structure, and reading order in the Markdown output. For complex documents with borderless tables or scanned pages, use hybrid mode (hybrid="docling-fast") for higher accuracy. The output is clean enough to feed directly into LLM context windows or RAG chunking pipelines.

Yes. OpenDataLoader is the first open-source tool that automates PDF accessibility end-to-end. Built in collaboration with PDF Association and Dual Lab (veraPDF developers), auto-tagging follows the Well-Tagged PDF specification and is validated programmatically using veraPDF. The layout analysis engine detects document structure (headings, tables, lists, reading order) and generates accessibility tags automatically. Auto-tagging converts untagged PDFs into Tagged PDFs under Apache 2.0 — no proprietary SDK dependency. Use format="tagged-pdf" (Python/Node.js) or --format tagged-pdf (CLI). For organizations needing full PDF/UA compliance, enterprise add-ons provide PDF/UA export and a visual tag editor. This replaces manual remediation workflows that typically cost $50–200+ per document.

Yes. Existing tools either depend on proprietary SDKs for writing structure tags, only output non-PDF formats (e.g., Docling outputs Markdown/JSON but cannot produce Tagged PDFs), or require manual intervention. OpenDataLoader is the first to do layout analysis → tag generation → Tagged PDF output entirely under an open-source license (Apache 2.0), with no proprietary dependency. Auto-tagging follows the PDF Association's Well-Tagged PDF specification and is validated using veraPDF, the industry-reference open-source PDF/A and PDF/UA validator.

OpenDataLoader provides an end-to-end pipeline: audit existing PDFs for tags (use_struct_tree=True), auto-tag untagged PDFs into Tagged PDFs (format="tagged-pdf", free under Apache 2.0), and export as PDF/UA-1 or PDF/UA-2 (enterprise add-on). Auto-tagging follows the PDF Association's Well-Tagged PDF specification and is validated using veraPDF. Auto-tagging generates the Tagged PDF; PDF/UA export is the final step. Contact us for enterprise integration.

The European Accessibility Act requires accessible digital products by June 28, 2025. OpenDataLoader supports the full remediation workflow: audit → auto-tag → Tagged PDF → PDF/UA export. Auto-tagging follows the PDF Association's Well-Tagged PDF specification and is validated using veraPDF, ensuring standards-compliant output. Auto-tagging to Tagged PDF is open-source under Apache 2.0. PDF/UA export and accessibility studio are enterprise add-ons. See our Accessibility Guide.

The core library is open-source under Apache 2.0 — free for commercial use. This includes all extraction features (text, tables, images, OCR, formulas, charts via hybrid mode), AI safety filters, Tagged PDF support, and auto-tagging to Tagged PDF. We are committed to keeping the core accessibility pipeline (layout analysis → auto-tagging → Tagged PDF) free and open-source. Enterprise add-ons (PDF/UA export, accessibility studio) are available for organizations needing end-to-end regulatory compliance.

MPL 2.0 requires file-level copyleft, which often triggers legal review before enterprise adoption. Apache 2.0 is fully permissive — no copyleft obligations, easier to integrate into commercial projects. If you are using a pre-2.0 version, it remains under MPL 2.0 and you can continue using it. Upgrading to 2.0+ means your project follows Apache 2.0 terms, which are strictly more permissive — no additional obligations, no action needed on your side.

• Quick Start (Python)
• Quick Start (Node.js)
• Quick Start (Java)
• JSON Schema Reference
• CLI Options
• Hybrid Mode Guide
• Tagged PDF Support
• AI Safety Features
• PDF Accessibility

We welcome contributions! See CONTRIBUTING.md for guidelines.

Apache License 2.0

Note: Versions prior to 2.0 are licensed under the Mozilla Public License 2.0.

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