README.md

---
tags:
- ocr
- document-processing
- lighton-ocr-2
- markdown
- uv-script
- generated
configs:
- config_name: dots-ocr
  data_files:
  - split: train
    path: dots-ocr/train-*
dataset_info:
  config_name: dots-ocr
  features:
  - name: image
    dtype: image
  - name: drawer_id
    dtype: string
  - name: card_number
    dtype: int64
  - name: filename
    dtype: string
  - name: text
    dtype: string
  - name: has_ocr
    dtype: bool
  - name: source
    dtype: string
  - name: source_url
    dtype: string
  - name: ia_collection
    dtype: string
  - name: markdown
    dtype: string
  - name: inference_info
    dtype: string
  splits:
  - name: train
    num_bytes: 14617601
    num_examples: 50
  download_size: 14452619
  dataset_size: 14617601
---

# Document OCR using LightOnOCR-2-1B

This dataset contains OCR results from images in [biglam/rubenstein-manuscript-catalog](https://huggingface.co/datasets/biglam/rubenstein-manuscript-catalog) using LightOnOCR-2, a fast and compact 1B OCR model trained with RLVR.

## Processing Details

- **Source Dataset**: [biglam/rubenstein-manuscript-catalog](https://huggingface.co/datasets/biglam/rubenstein-manuscript-catalog)
- **Model**: [lightonai/LightOnOCR-2-1B](https://huggingface.co/lightonai/LightOnOCR-2-1B)
- **Number of Samples**: 50
- **Processing Time**: 6.4 min
- **Processing Date**: 2026-02-15 00:39 UTC

### Configuration

- **Image Column**: `image`
- **Output Column**: `markdown`
- **Dataset Split**: `train`
- **Batch Size**: 16
- **Target Image Size**: 1540px (longest dimension)
- **Max Model Length**: 8,192 tokens
- **Max Output Tokens**: 4,096
- **Temperature**: 0.2
- **Top P**: 0.9
- **GPU Memory Utilization**: 80.0%

## Model Information

LightOnOCR-2 is a next-generation fast, compact OCR model that excels at:
- ⚡ **Fastest Speed** - 42.8 pages/second on H100 GPU (7× faster than v1)
- 🎯 **High Accuracy** - 83.2 ± 0.9% on OlmOCR-Bench (+7.1% vs v1)
- 🧠 **RLVR Training** - Eliminates repetition loops and formatting errors
- 📚 **Better Dataset** - 2.5× larger training data with cleaner annotations
- 📐 **LaTeX formulas** - Mathematical notation in LaTeX format
- 📊 **Tables** - Extracted and formatted as markdown
- 📝 **Document structure** - Hierarchy and layout preservation
- 🌍 **Multilingual** - Optimized for European languages
- 💪 **Production-ready** - Outperforms models 9× larger

### Key Improvements over v1

- **7.5× faster**: 42.8 vs 5.71 pages/sec on H100
- **+7.1% accuracy**: 83.2% vs 76.1% on benchmarks
- **Better quality**: RLVR training eliminates common OCR errors
- **Cleaner output**: No repetition loops or formatting glitches
- **Simpler**: Single model (no vocabulary variants)

## Dataset Structure

The dataset contains all original columns plus:
- `markdown`: The extracted text in markdown format with LaTeX formulas
- `inference_info`: JSON list tracking all OCR models applied to this dataset

## Usage

```python
from datasets import load_dataset
import json

# Load the dataset
dataset = load_dataset("{output_dataset_id}", split="train")

# Access the markdown text
for example in dataset:
    print(example["markdown"])
    break

# View all OCR models applied to this dataset
inference_info = json.loads(dataset[0]["inference_info"])
for info in inference_info:
    print(f"Column: {info['column_name']} - Model: {info['model_id']}")
```

## Reproduction

This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) LightOnOCR-2 script:

```bash
uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \
    biglam/rubenstein-manuscript-catalog \
    <output-dataset> \
    --image-column image \
    --batch-size 16
```

## Performance

- **Processing Speed**: ~0.13 images/second
- **Benchmark Score**: 83.2 ± 0.9% on OlmOCR-Bench
- **Training**: RLVR (Reinforcement Learning with Verifiable Rewards)

Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)