opendataloader-pdf

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

Capability Matrix

Capability	Supported	Tier
Data extraction
Extract text with correct reading order	Yes	Free
Bounding boxes for every element	Yes	Free
Table extraction (simple borders)	Yes	Free
Table extraction (complex/borderless)	Yes	Free (Hybrid)
Heading hierarchy detection	Yes	Free
List detection (numbered, bulleted, nested)	Yes	Free
Image extraction with coordinates	Yes	Free
AI chart/image description	Yes	Free (Hybrid)
OCR for scanned PDFs	Yes	Free (Hybrid)
Formula extraction (LaTeX)	Yes	Free (Hybrid)
Tagged PDF structure extraction	Yes	Free
AI safety (prompt injection filtering)	Yes	Free
Header/footer/watermark filtering	Yes	Free
Accessibility
Auto-tagging → Tagged PDF for untagged PDFs	Yes	Free (Apache 2.0)
PDF/UA-1, PDF/UA-2 export	💼 Available	Enterprise
Accessibility studio (visual editor)	💼 Available	Enterprise
Limitations
Process Word/Excel/PPT	No	—
GPU required	No	—

Extraction Benchmarks

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

Engine	Overall	Reading Order	Table	Heading	Speed (s/page)	License
opendataloader [hybrid]	0.907	0.934	0.928	0.821	0.463	Apache-2.0
nutrient	0.885	0.925	0.708	0.819	0.008	Commercial
docling	0.882	0.898	0.887	0.824	0.762	MIT
marker	0.861	0.890	0.808	0.796	53.932	GPL-3.0
unstructured [hi_res]	0.841	0.904	0.588	0.749	3.008	Apache-2.0
edgeparse	0.837	0.894	0.717	0.706	0.036	Apache-2.0
opendataloader	0.831	0.902	0.489	0.739	0.015	Apache-2.0
mineru	0.831	0.857	0.873	0.743	5.962	AGPL-3.0
pymupdf4llm	0.732	0.885	0.401	0.412	0.091	AGPL-3.0
unstructured	0.686	0.882	0.000	0.388	0.077	Apache-2.0
markitdown	0.589	0.844	0.273	0.000	0.114	MIT
liteparse	0.576	0.866	0.000	0.000	1.061	Apache-2.0

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

Which Mode Should I Use?

Your Document	Mode	Install	Server Command	Client Command
Standard digital PDF	Fast (default)	pip install opendataloader-pdf	None needed	opendataloader-pdf file1.pdf file2.pdf folder/
Complex or nested tables	Hybrid	pip install "opendataloader-pdf[hybrid]"	opendataloader-pdf-hybrid --port 5002	opendataloader-pdf --hybrid docling-fast file1.pdf file2.pdf folder/
Scanned / image-based PDF	Hybrid + OCR	pip install "opendataloader-pdf[hybrid]"	opendataloader-pdf-hybrid --port 5002 --force-ocr	opendataloader-pdf --hybrid docling-fast file1.pdf file2.pdf folder/
Non-English scanned PDF	Hybrid + OCR	pip install "opendataloader-pdf[hybrid]"	opendataloader-pdf-hybrid --port 5002 --force-ocr --ocr-engine <engine> --ocr-lang <codes> — see Choosing an OCR Engine	opendataloader-pdf --hybrid docling-fast file1.pdf file2.pdf folder/
Mathematical formulas	Hybrid + formula	pip install "opendataloader-pdf[hybrid]"	opendataloader-pdf-hybrid --enrich-formula	opendataloader-pdf --hybrid docling-fast --hybrid-mode full file1.pdf file2.pdf folder/
Charts needing description	Hybrid + picture	pip install "opendataloader-pdf[hybrid]"	opendataloader-pdf-hybrid --enrich-picture-description	opendataloader-pdf --hybrid docling-fast --hybrid-mode full file1.pdf file2.pdf folder/
Nested sections for RAG chunking	Hybrid + heading levels	pip install "opendataloader-pdf[hybrid]"	opendataloader-pdf-hybrid --port 5002 --heading-hierarchy	opendataloader-pdf --hybrid docling-fast file1.pdf file2.pdf folder/
Untagged PDFs needing accessibility	Auto-tagging → Tagged PDF	pip install opendataloader-pdf	None needed	opendataloader-pdf --format tagged-pdf file1.pdf file2.pdf folder/

Quick Start

Python

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"
)

Node.js

npm install @opendataloader/pdf

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

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

Java

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

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

Hybrid Mode: #1 Accuracy for Complex PDFs

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"
)

Python, without a second terminal — HybridServer starts the backend server for the time of a with block, and stops it after the block:

import opendataloader_pdf

# The server loads its models once. The first start can take minutes while the models download
with opendataloader_pdf.HybridServer() as server:
    opendataloader_pdf.convert(
        input_path=["file1.pdf", "file2.pdf", "folder/"],
        output_dir="output/",
        hybrid="docling-fast",
        hybrid_url=server.url
    )

Give server options in args, as for opendataloader-pdf-hybrid: HybridServer(args=["--force-ocr", "--ocr-lang", "ko,en"]). The server listens on 127.0.0.1 at a free port (port= sets the port). Its log goes to stderr.

OCR for Scanned PDFs

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"

The language codes above are for the default engine (EasyOCR): en, ko, ja, ch_sim, ch_tra, de, fr, ar, and more.

Choosing an OCR Engine

OCR quality varies with the engine, the language, and the document itself (scan resolution, fonts, layout). No engine is best for every document, so run a few representative pages through several engines and compare the output before processing a whole collection.

Select the engine with --ocr-engine. Each engine uses its own language code system for --ocr-lang:

Engine	--ocr-engine	--ocr-lang example	Platform / extra install
EasyOCR (default)	easyocr	ko,en (ISO 639-1)	Included in opendataloader-pdf[hybrid]
RapidOCR	rapidocr	korean (RapidOCR codes: en, ch, japan, korean, ...)	pip install rapidocr onnxruntime
Tesseract (CLI)	tesseract	kor,eng (ISO 639-2)	Tesseract binary + language data (e.g. tesseract-ocr-kor)
Tesseract (Python binding)	tesserocr	kor,eng (ISO 639-2)	pip install tesserocr + libtesseract
Apple Vision	ocrmac	ko-KR,en-US (BCP-47)	macOS only, pip install ocrmac
NVIDIA Nemotron OCR	nemotron-ocr	multilingual or english	Linux x86_64 + CUDA, pip install "docling[feat-ocr-nemotron]"
Automatic	auto	Not configurable	Docling picks an available engine

To compare engines on a sample, start one server per engine and send the same file to each.

Terminal 1 — EasyOCR server:

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

Terminal 2 — Tesseract server:

opendataloader-pdf-hybrid --port 5003 --force-ocr --ocr-engine tesseract --ocr-lang "kor,eng"

Terminal 3 — Send the same sample to both:

opendataloader-pdf sample.pdf --hybrid docling-fast --hybrid-mode full --hybrid-url http://localhost:5002 -o out-easyocr
opendataloader-pdf sample.pdf --hybrid docling-fast --hybrid-mode full --hybrid-url http://localhost:5003 -o out-tesseract

Formula Extraction (LaTeX)

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.

Chart & Image Description

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": "image",
  "pdfua_tag": "Figure",
  "id": 285,
  "page number": 1,
  "bounding box": [72.0, 400.0, 540.0, 650.0],
  "alt": "A bar chart showing waste generation by region from 2016 to 2030...",
  "alt_source": "ai-generated"
}

alt_source says where the text came from: original (the PDF's own /Alt), ai-generated, or missing — and when it is missing, there is no alt field at all.

With --enrich-picture-description, every picture goes to the model whatever its size. The server reports the tally per request, so a picture that came back without a description shows up in the log rather than only in the output:

picture_description pictures=4 described=4

Cost follows the number of pictures, not their size: a 40×20 pt logo costs about what a full-page chart does, and on CPU one picture can take over a minute. A document full of icons is therefore minutes of work per page, and conversions are serialized, so other requests wait. --picture-area-threshold 0.05 spends the model only on pictures covering at least 5% of their page:

picture_description pictures=4 described=1 skipped=3 threshold=0.05

Skips are logged at INFO when you set a threshold, and at WARNING when you did not — in that case nothing was asked to drop them, so the cause is worth a look.

Uses SmolVLM (256M), a lightweight vision model. Custom prompts supported via --picture-description-prompt. Since the picture count comes from the uploaded file, a server reachable by untrusted callers should set --picture-area-threshold, and --max-file-size to cap the conversion work one file can ask for. That limit is applied once the request body has been received, so it bounds conversion cost rather than upload bandwidth or temporary disk.

Heading Hierarchy

The layout model labels a region as a section header without a depth, so by default every heading comes back at level 1 and subsections sit at the same depth as the document title. Flat headings make it hard to tell a section title from a document title when chunking for RAG.

--heading-hierarchy infers the depth — from the PDF outline first, then section numbering (1. → 1.1 → 1.1.1), then visual style:

# Server
opendataloader-pdf-hybrid --port 5002 --heading-hierarchy

# Client — no extra flag needed
opendataloader-pdf --hybrid docling-fast file1.pdf file2.pdf folder/

  # 3. Methodology
- # 3.1. Multi-Object Rectification Network
+ ## 3.1. Multi-Object Rectification Network
- # Abstract
+ ## Abstract

Numbering covers numbered sections; style is what pulls an unnumbered Abstract or References out from under the document title.

Off by default, so existing output is unchanged. Levels are capped at H6.

Hancom Data Loader Integration — Coming Soon

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

Output Formats

Format	Use Case
JSON	Structured data with bounding boxes, semantic types
Markdown	Clean text for LLM context, RAG chunks
HTML	Web display with styling
Annotated PDF	Visual debugging — see detected structures (sample)
Text	Plain text extraction

Combine formats: format="json,markdown"

JSON Output Example

{
  "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"
}

Field	Description
type	Element type: heading, paragraph, table, list, image, caption, formula
id	Unique identifier for cross-referencing
page number	1-indexed page reference
bounding box	[left, bottom, right, top] in PDF points (72pt = 1 inch)
heading level	Heading depth (1+)
content	Extracted text

Full JSON Schema

Advanced Features

Tagged PDF Support

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

AI Safety: Prompt Injection Protection

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

Custom Sanitization Rules

To use your own rules, give a JSON rules file with --sanitize-rules (Python: sanitize_rules, Node.js: sanitizeRules). This option also turns on --sanitize. Only the rules of the file run, in place of the built-in rules.

{
  "rules": [
    { "id": "kr-mobile", "pattern": "(?<!\\d)01[016789]-\\d{3,4}-\\d{4}(?!\\d)", "replacement": "010-0000-0000" }
  ]
}

opendataloader-pdf file1.pdf folder/ --sanitize-rules rules.json

To keep the built-in rules, start from examples/sanitize-rules/default-rules.json. It has the built-in rules of --sanitize. Copy it, then add, change, or delete rules. If a later release changes the built-in rules, compare your copy with the new template.

Each rule has three keys:

Key	Value
id	1 to 64 characters from a-z, 0-9, and -. Each ID only once
pattern	A Java regular expression, 1 to 5,000 characters
replacement	Literal text, 1 to 200 characters, with no control characters ($1 is not a group reference)

The run stops before any file is processed if the rules file has one of these problems:

• An unknown or missing key, or a value of the wrong type.
• A rule ID that is repeated or has a wrong format.
• A pattern that is empty or does not compile, or a replacement that is empty or has a control character.
• A value over a limit: a file larger than 1 MiB (1,048,576 bytes), more than 200 rules, or a pattern or replacement that is too long.
• No rules.

The error gives the reason, and the rule or the key.

How the rules work:

• All rules run on the original text of each line. A match cannot cross a line, and a rule does not see the placeholder of another rule.
• If two matches overlap, the match that starts first is kept. If they start at the same position, the longer match is kept. If they also have the same length, the match of the earlier rule in the file is kept.
• Rules apply to the extracted text. They do not apply to image descriptions, formulas, or document metadata such as the title.

Warning: A pattern runs on every text line, with no time limit. Some patterns are very slow, also on a short line. For example, nested repetition with limits such as (a{1,20}){1,20}$ can take tens of seconds on a line of 31 characters, and the time grows fast with the length of the line. Some patterns overflow the stack on a long line, for example (a|b)*c. Then that file fails with an error that names the rule, and the run continues with the next file. Use only rules files that you trust. Test each new pattern on lines as long as your real text.

\w matches only ASCII letters, digits, and _. On Java 19 and later, \b uses the same ASCII rule. On Java 11 to 18, \b also counts other letters, such as Hangul, as word characters. Use (?<!\d) and (?!\d) around numbers.

AI Safety Guide

LangChain Integration

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

Advanced Options

# 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

PDF Accessibility & PDF/UA Conversion

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)

Regulation	Deadline	Requirement
European Accessibility Act (EAA)	June 28, 2025	Accessible digital products across the EU
ADA & Section 508	In effect	U.S. federal agencies and public accommodations
Digital Inclusion Act	In effect	South Korea digital service accessibility

Standards & Validation

Aspect	Detail
Specification	Well-Tagged PDF by PDF Association
Validation	veraPDF — industry-reference open-source PDF/A & PDF/UA validator
Collaboration	PDF Association + Dual Lab (veraPDF developers) co-develop tagging and validation
License	Auto-tagging → Tagged PDF: Apache 2.0 (free). PDF/UA export: Enterprise

Accessibility Pipeline

Step	Feature	Status	Tier
1. Audit	Read existing PDF tags, detect untagged PDFs	Shipped	Free
2. Auto-tag → Tagged PDF	Generate structure tags for untagged PDFs	Shipped	Free (Apache 2.0)
3. Export PDF/UA	Convert to PDF/UA-1 or PDF/UA-2 compliant files	💼 Available	Enterprise
4. Visual editing	Accessibility studio — review and fix tags	💼 Available	Enterprise

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

Auto-Tagging

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".

End-to-End Compliance Workflow

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

Roadmap

Feature	Timeline	Tier
Hancom Data Loader — Enterprise AI document analysis, customer-customized models, VLM-based chart/image understanding, production-grade OCR	Q2-Q3 2026	Planned
Structure validation — Verify PDF tag trees	Q3 2026	Planned

Full Roadmap

Frequently Asked Questions

What is the best PDF parser for RAG?

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.

What is the best open-source PDF parser?

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.

How do I extract tables from PDF for LLM?

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
)

How does it compare to docling, marker, or pymupdf4llm?

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.

Can I use this without sending data to the cloud?

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.

Does it support OCR for scanned PDFs?

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, with a choice of OCR engines via --ocr-engine. Quality varies by engine, language, and document — see Choosing an OCR Engine.

Does it work with Korean, Japanese, or Chinese documents?

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), and compare OCR engines on a sample of your documents — see Choosing an OCR Engine. 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.

How fast is it?

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.

Does it handle multi-column layouts?

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.

What is hybrid mode?

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.

Does it work with LangChain?

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

How do I chunk PDFs for RAG?

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.

How do I cite PDF sources in RAG answers?

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.

How do I convert PDF to Markdown for LLM?

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.

Is there an automated PDF accessibility remediation tool?

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.

Is this really the first open-source PDF auto-tagging tool?

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.

How do I convert existing PDFs to PDF/UA?

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.

How do I make my PDFs accessible for EAA compliance?

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.

Is OpenDataLoader PDF free?

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.

Why did the license change from MPL 2.0 to Apache 2.0?

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.

Documentation

• 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

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

Apache License 2.0

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

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