OCR to Markdown with Nanonets

Convert document images to structured markdown using Nanonets-OCR-s with vLLM acceleration.

Quick Start

# Basic OCR conversion
uv run main.py document-images markdown-output

# With custom image column
uv run main.py scanned-docs extracted-text --image-column page

# Test with subset
uv run main.py large-dataset test-output --max-samples 100

# Run directly from Hub
uv run https://huggingface.co/datasets/davanstrien/dataset-creation-scripts/raw/main/ocr-vllm/main.py \
  input-dataset output-dataset

Features

Nanonets-OCR-s excels at:

• LaTeX equations: Mathematical formulas preserved in LaTeX format
• Tables: Complex table structures converted to markdown
• Document structure: Headers, lists, and formatting maintained
• Special elements: Signatures, watermarks, and checkboxes detected

HF Jobs Deployment

Deploy on GPU infrastructure:

hfjobs run \
  --flavor l4x1 \
  --secret HF_TOKEN=$HF_TOKEN \
  ghcr.io/astral-sh/uv:latest \
  /bin/bash -c "
    uv run https://huggingface.co/datasets/davanstrien/dataset-creation-scripts/raw/main/ocr-vllm/main.py \
      your-document-dataset \
      your-markdown-output \
      --batch-size 32 \
      --gpu-memory-utilization 0.8
  "

Parameters

Examples

Scientific Papers

uv run main.py arxiv-papers arxiv-markdown \
  --max-tokens 8192  # Longer output for equations

Scanned Documents

uv run main.py historical-scans extracted-text \
  --image-column scan \
  --batch-size 4  # Lower batch for high-res images

Multi-page Documents

uv run main.py pdf-pages document-text \
  --image-column page_image \
  --batch-size 16

Tips

• Batch size: Reduce if encountering OOM errors
• GPU memory: Increase for better throughput
• Max tokens: Increase for long documents
• Testing: Use --max-samples to validate pipeline

Model Details

Nanonets-OCR-s (576M parameters) is optimized for:

• High-quality markdown output
• Complex document understanding
• Efficient GPU inference
• Multi-language support

For more details, see the model card.