id stringclasses 2
values | side stringclasses 2
values | image imagewidth (px) 576 576 | markdown stringclasses 2
values | inference_info stringclasses 1
value |
|---|---|---|---|---|
permis-recto | recto | <div style="text-align: right; margin-top: -10px; margin-right: 20px;">
<div style="font-size: 10px; line-height: 1.2;">
55 344 3122<br>
CAN 373473
</div>
</div>
<div style="text-align: center; margin-top: 200px; margin-bottom: 200px;">
<table border="1" cellpadding="5" cellspacing="0" styl... | [{"model_id": "lightonai/LightOnOCR-2-1B", "model_name": "LightOnOCR-2", "column_name": "markdown", "timestamp": "2026-08-11T13:50:53.497995", "temperature": 0.2, "top_p": 0.9, "max_tokens": 4096, "target_size": 1540}] | |
permis-verso | verso | <table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>الأقسام</th>
<th>تاريخ التعلم</th>
<th>المقيّمات</th>
</tr>
<tr>
<th>Catégories</th>
<th>Date de délivrance</th>
<th>Restrictions</th>
</tr>
</thead>
<tbody>
<tr>
<td>A1</... | [{"model_id": "lightonai/LightOnOCR-2-1B", "model_name": "LightOnOCR-2", "column_name": "markdown", "timestamp": "2026-08-11T13:50:53.497995", "temperature": 0.2, "top_p": 0.9, "max_tokens": 4096, "target_size": 1540}] |
Document OCR using LightOnOCR-2-1B
This dataset contains OCR results from images in mehdigououiad/permis-ocr-bench using LightOnOCR-2, a fast and compact 1B OCR model trained with RLVR.
Processing Details
- Source Dataset: mehdigououiad/permis-ocr-bench
- Model: lightonai/LightOnOCR-2-1B
- Number of Samples: 2
- Processing Time: 1.8 min
- Processing Date: 2026-08-11 13:50 UTC
Configuration
- Image Column:
image - Output Column:
markdown - Dataset Split:
train - Batch Size: 16
- Target Image Size: 1540px (longest dimension)
- Max Model Length: 16,384 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 formulasinference_info: JSON list tracking all OCR models applied to this dataset
Usage
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 LightOnOCR-2 script:
uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr2.py \
mehdigououiad/permis-ocr-bench \
<output-dataset> \
--image-column image \
--batch-size 16
Performance
- Processing Speed: ~0.02 images/second
- Benchmark Score: 83.2 ± 0.9% on OlmOCR-Bench
- Training: RLVR (Reinforcement Learning with Verifiable Rewards)
Generated with 🤖 UV Scripts
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