README.md

metadata

viewer: false
tags:
  - uv-script
  - ocr
  - vision-language-model
  - document-processing

OCR UV Scripts

Part of uv-scripts - ready-to-run ML tools powered by UV

Ready-to-run OCR scripts that work with uv run - no setup required!

🚀 Quick Start with HuggingFace Jobs

Run OCR on any dataset without needing your own GPU:

# Quick test with 10 samples
hf jobs uv run --flavor l4x1 \
    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
    your-input-dataset your-output-dataset \
    --max-samples 10

That's it! The script will:

• ✅ Process first 10 images from your dataset
• ✅ Add OCR results as a new markdown column
• ✅ Push the results to a new dataset
• 📊 View results at: https://huggingface.co/datasets/[your-output-dataset]

📋 Available Scripts

Nanonets OCR (nanonets-ocr.py)

State-of-the-art document OCR using nanonets/Nanonets-OCR-s that handles:

• 📐 LaTeX equations - Mathematical formulas preserved
• 📊 Tables - Extracted as HTML format
• 📝 Document structure - Headers, lists, formatting maintained
• 🖼️ Images - Captions and descriptions included
• ☑️ Forms - Checkboxes rendered as ☐/☑

dots.ocr (dots-ocr.py)

Advanced document layout analysis and OCR using rednote-hilab/dots.ocr that provides:

• 🎯 Layout detection - Bounding boxes for all document elements
• 📑 Category classification - Text, Title, Table, Formula, Picture, etc.
• 📖 Reading order - Preserves natural reading flow
• 🌍 Multilingual support - Handles multiple languages seamlessly
• 🔧 Flexible output - JSON, structured columns, or markdown

💻 Usage Examples

Run on HuggingFace Jobs (Recommended)

No GPU? No problem! Run on HF infrastructure:

# Basic OCR job
hf jobs uv run --flavor l4x1 \
    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
    your-input-dataset your-output-dataset

# Document layout analysis with dots.ocr
hf jobs uv run --flavor l4x1 \
    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \
    your-input-dataset your-layout-dataset \
    --mode layout-all \
    --output-format structured \
    --use-transformers  # More compatible backend

# Real example with UFO dataset 🛸
hf jobs uv run \
    --flavor a10g-large \
    --image vllm/vllm-openai:latest \
    -s HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \
    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
    davanstrien/ufo-ColPali \
    your-username/ufo-ocr \
    --image-column image \
    --max-model-len 16384 \
    --batch-size 128

# Private dataset with custom settings
hf jobs uv run --flavor l40sx1 \
    -s HF_TOKEN=$(python3 -c "from huggingface_hub import get_token; print(get_token())") \
    https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
    private-input private-output \
    --private \
    --batch-size 32

Python API

from huggingface_hub import run_uv_job

job = run_uv_job(
    "https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py",
    args=["input-dataset", "output-dataset", "--batch-size", "16"],
    flavor="l4x1"
)

Run Locally (Requires GPU)

# Clone and run
git clone https://huggingface.co/datasets/uv-scripts/ocr
cd ocr
uv run nanonets-ocr.py input-dataset output-dataset

# Or run directly from URL
uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/nanonets-ocr.py \
    input-dataset output-dataset

# dots.ocr examples
uv run dots-ocr.py documents analyzed-docs  # Full layout + OCR
uv run dots-ocr.py scans layouts --mode layout-only  # Layout only
uv run dots-ocr.py papers markdown --output-format markdown  # As markdown

📁 Works With

Any HuggingFace dataset containing images - documents, forms, receipts, books, handwriting.

🎛️ Configuration Options

Common Options (Both Scripts)

Option	Default	Description
--image-column	image	Column containing images
--batch-size	32	Images processed together
--max-model-len	8192/24000*	Max context length
--max-tokens	4096/16384*	Max output tokens
--gpu-memory-utilization	0.8	GPU memory usage (0.0-1.0)
--split	train	Dataset split to process
--max-samples	None	Limit samples (for testing)
--private	False	Make output dataset private

*dots.ocr uses higher defaults (24000/16384)

dots.ocr Specific Options

Option	Default	Description
--mode	layout-all	Processing mode: layout-all, layout-only, ocr, grounding-ocr
--output-format	json	Output format: json, structured, markdown
--filter-category	None	Filter by layout category (e.g., Table, Formula)
--output-column	dots_ocr_output	Column name for JSON output
--bbox-column	layout_bboxes	Column for bounding boxes (structured mode)
--category-column	layout_categories	Column for categories (structured mode)
--text-column	layout_texts	Column for texts (structured mode)
--markdown-column	markdown	Column for markdown output
--use-transformers	False	Use transformers backend instead of vLLM (more compatible)

💡 Performance tip: Increase batch size for faster processing (e.g., --batch-size 128 for A10G GPUs)

⚠️ dots.ocr Note: If you encounter vLLM initialization errors, use --use-transformers for a more compatible (but slower) backend.

More OCR VLM Scripts coming soon! Stay tuned for updates!