File size: 2,749 Bytes
975e471 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | # 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). |