ocrrouter 0.1.3

A practical tool for converting PDF to Markdown

OCRRouter

A powerful Python library for converting PDFs and images to Markdown using multiple expert VLM backends

What is OCRRouter?

OCRRouter is a production-ready document processing library that converts PDFs and images to high-quality Markdown. It stands out with:

• 6 Expert VLM Backends — Choose from MinerU, DeepSeek-OCR, DotsOCR, PaddleOCR, Hunyuan-OCR, or GeneralVLM (GPT/Claude/Gemini)
• Composite Mode — Mix layout detection from one model with OCR from another for optimal results (unique feature!)
• Rich Document Support — Tables, formulas, images, code blocks, lists, and complex layouts
• Flexible APIs — Sync/async, single/batch processing, multiple output formats
• Production Ready — Built-in observability (Langfuse), retries, error handling, debug mode

Quick Start

Installation

pip install ocrrouter

30-Second Example

from ocrrouter import process_document

# One-liner document conversion
result = process_document(
    "document.pdf",
    "output/",
    backend="deepseek",
    openai_api_key="your-api-key"
)

print(result["markdown"])

Basic Usage

from ocrrouter import DocumentPipeline, Settings

# Configure pipeline
settings = Settings(
    backend="deepseek",
    openai_base_url="https://api.example.com/v1",
    openai_api_key="your-api-key",
    output_mode="all"  # layout + OCR
)

# Process document
pipeline = DocumentPipeline(settings=settings)
result = pipeline.process("document.pdf", "output/")

# Access results
print(f"Markdown: {result['markdown'][:100]}...")
print(f"Output directory: {result['output_dir']}")

Async Processing

# Async processing for better performance
result = await pipeline.aio_process("document.pdf", "output/")

# Batch processing with concurrency control
results = await pipeline.aio_process_batch(
    ["doc1.pdf", "doc2.pdf", "doc3.pdf"],
    "output/",
    session_id="batch-001"
)

Key Features

1. Multiple Expert Backends

Each backend is optimized for different document types:

Backend	Layout	OCR	Best For
MinerU	✓	✓	Academic papers, complex layouts, formulas
DeepSeek	✓	✓	General documents, efficiency, grounding mode
DotsOCR	✓	✓	Flexible extraction (one-step or two-step)
PaddleOCR	—	✓	Fast OCR, multilingual support
Hunyuan	—	✓	Markdown-optimized output
GeneralVLM	—	✓	GPT-4V, Claude, Gemini, custom VLMs

2. Composite Mode (Mix & Match)

Combine the strengths of different models:

settings = Settings(
    backend="composite",
    layout_model="mineru",      # Best layout detection
    ocr_model="paddleocr",      # Fast OCR extraction
)

Why use composite mode?

• Optimize for cost vs quality
• Leverage each model's strengths
• Example: MinerU's excellent layout + PaddleOCR's speed
• 2-3x faster than single-model approaches in many cases

3. Three Output Modes

Control processing behavior:

# Full layout + OCR (default)
Settings(output_mode="all")

# Layout detection only
Settings(output_mode="layout_only")

# Direct OCR without layout analysis
Settings(output_mode="ocr_only")

4. Rich Output Formats

Multiple output files for different use cases:

• Markdown (.md) — Human-readable converted text
• Layout PDF (_layout.pdf) — Visual layout with bounding boxes
• Model JSON (_model.json) — Raw model output
• Middle JSON (_middle.json) — Processed structural data
• Content List (_content_list.json) — Simplified flat structure
• Images — Extracted figures, tables, equations

Use Cases

Academic Research

Extract formulas, citations, and complex layouts from research papers and theses:

settings = Settings(
    backend="mineru",
    formula_enable=True,
    table_merge_enable=True  # Cross-page table merging
)

Business Documents

Parse invoices, contracts, and forms with table extraction:

settings = Settings(
    backend="deepseek",
    table_enable=True,
    output_mode="all"
)

Document Digitization

Batch process archives with multilingual support:

settings = Settings(
    backend="composite",
    layout_model="deepseek",
    ocr_model="paddleocr",  # Strong multilingual support
    max_concurrency=10
)

AI/ML Pipelines

Extract structured data for RAG or training:

settings = Settings(
    backend="deepseek",
    dump_content_list=True,  # Simplified JSON for ML
    dump_middle_json=True     # Structured data
)

Backend Selection Guide

How to Choose?

Need layout detection + OCR?

• Academic/Scientific → MinerU (best formula extraction)
• General documents → DeepSeek (efficient grounding mode)
• Flexible extraction → DotsOCR (one-step or two-step)

Need OCR only?

• Fast processing → PaddleOCR
• Markdown-focused → Hunyuan
• Use GPT-4/Claude → GeneralVLM

Want to optimize cost/speed?

• Use Composite Mode: strong layout + fast OCR

See Backend Guide for detailed comparison.

Documentation

• Backend Guide — Model comparison and selection
• Examples — Code examples and recipes
• API Reference — Complete API documentation
• Configuration — Settings and environment variables
• Output Formats — Understanding output files

Configuration

OCRRouter uses explicit configuration (no automatic .env loading):

from ocrrouter import Settings

# Method 1: Settings object
settings = Settings(
    backend="deepseek",
    openai_api_key="your-key",
    max_concurrency=20,
    http_timeout=120,
    max_retries=3
)

# Method 2: Constructor arguments
pipeline = DocumentPipeline(
    backend="deepseek",
    openai_api_key="your-key"
)

# Method 3: Settings with overrides
pipeline = DocumentPipeline(
    settings=settings,
    max_concurrency=50  # Override
)

See Configuration Guide for all available settings.

Advanced Features

Observability with Langfuse

from langfuse import Langfuse
from ocrrouter import DocumentPipeline, Settings

langfuse = Langfuse(
    public_key="pk-...",
    secret_key="sk-...",
    host="https://cloud.langfuse.com"
)

settings = Settings(backend="deepseek", openai_api_key="your-key")
pipeline = DocumentPipeline(settings=settings, langfuse=langfuse)

# Traces appear in Langfuse dashboard
result = await pipeline.aio_process("document.pdf", "output/")

Error Handling & Debug Mode

settings = Settings(
    backend="deepseek",
    max_retries=5,
    debug=True,           # Save failed requests
    debug_dir="./debug"   # Debug output location
)

Direct Backend Access

from ocrrouter import get_backend, Settings

settings = Settings(openai_api_key="your-key")
backend = get_backend("mineru", settings=settings)

# Advanced control
middle_json, model_output = await backend.analyze(pdf_bytes, image_writer)

Examples

See docs/EXAMPLES.md for comprehensive examples including:

• Basic document processing
• Batch processing with concurrency
• Composite mode configurations
• FastAPI integration
• Custom pipelines
• Use case-specific recipes

Or check out the demo scripts in demo/:

• demo/quickstart.py — Minimal example
• demo/composite_mode.py — Composite mode showcase
• demo/demo.py — Comprehensive demo

Requirements

• Python 3.10, 3.11, 3.12, or 3.13
• VLM server access (for backends requiring API calls)
• See pyproject.toml for full dependency list

Installation

# From PyPI
pip install ocrrouter

# From source
git clone https://github.com/yourusername/ocrrouter.git
cd ocrrouter
pip install -e .

Contributing

Contributions are welcome! See CONTRIBUTING.md for guidelines.

License

This project is licensed under the AGPL-3.0 License - see the LICENSE file for details.

Support

• Issues: Report bugs and request features via GitHub Issues
• Documentation: Full documentation at docs/
• Examples: See docs/EXAMPLES.md and demo/

Acknowledgments

We would like to thank the following projects for providing code and models:

• Dots.OCR
• PaddleOCR
• MinerU
• HunyuanOCR
• DeepSeek-OCR

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