| # OCR to Markdown with Nanonets | |
| Convert document images to structured markdown using [Nanonets-OCR-s](https://huggingface.co/nanonets/Nanonets-OCR-s) with vLLM acceleration. | |
| ## Quick Start | |
| ```bash | |
| # 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: | |
| ```bash | |
| 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 | |
| | Parameter | Default | Description | | |
| |-----------|---------|-------------| | |
| | `--image-column` | `"image"` | Column containing images | | |
| | `--batch-size` | `8` | Images per batch | | |
| | `--model` | `nanonets/Nanonets-OCR-s` | OCR model to use | | |
| | `--max-tokens` | `4096` | Max output tokens | | |
| | `--gpu-memory-utilization` | `0.7` | GPU memory usage | | |
| | `--split` | `"train"` | Dataset split | | |
| | `--max-samples` | None | Limit samples (testing) | | |
| | `--private` | False | Private output dataset | | |
| ## Examples | |
| ### Scientific Papers | |
| ```bash | |
| uv run main.py arxiv-papers arxiv-markdown \ | |
| --max-tokens 8192 # Longer output for equations | |
| ``` | |
| ### Scanned Documents | |
| ```bash | |
| uv run main.py historical-scans extracted-text \ | |
| --image-column scan \ | |
| --batch-size 4 # Lower batch for high-res images | |
| ``` | |
| ### Multi-page Documents | |
| ```bash | |
| 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](https://huggingface.co/nanonets/Nanonets-OCR-s). |