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:

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

That's it! The script will:

• ✅ Process all images in 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 ☐/☑

💻 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

# Real example with UFO dataset 🛸
hf jobs uv run \
    --flavor a10g-large \
    --image vllm/vllm-openai:latest \
    -e 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 64

# Private dataset with custom settings
hf jobs uv run --flavor l40sx1 \
    -e 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

📁 Works With

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

🎛️ Configuration Options

Option	Default	Description
--image-column	image	Column containing images
--batch-size	8	Images processed together
--max-model-len	8192	Max context length
--max-tokens	4096	Max output tokens
--gpu-memory-utilization	0.7	GPU memory usage
--split	train	Dataset split to process
--max-samples	None	Limit samples (for testing)
--private	False	Make output dataset private

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