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---
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)
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