README.md

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

# OCR UV Scripts

> Part of [uv-scripts](https://huggingface.co/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:

```bash
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](https://huggingface.co/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:

```bash
# 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

```python
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)

```bash
# 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!