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README.md

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+---
+tags:
+- ocr
+- document-processing
+- lighton-ocr-2
+- markdown
+- uv-script
+- generated
+---
+
+# Document OCR using LightOnOCR-2-1B
+
+This dataset contains OCR results from images in [NationalLibraryOfScotland/medical-history-of-british-india](https://huggingface.co/datasets/NationalLibraryOfScotland/medical-history-of-british-india) using LightOnOCR-2, a fast and compact 1B OCR model trained with RLVR.
+
+## Processing Details
+
+- **Source Dataset**: [NationalLibraryOfScotland/medical-history-of-british-india](https://huggingface.co/datasets/NationalLibraryOfScotland/medical-history-of-british-india)
+- **Model**: [lightonai/LightOnOCR-2-1B](https://huggingface.co/lightonai/LightOnOCR-2-1B)
+- **Number of Samples**: 10
+- **Processing Time**: 4.6 min
+- **Processing Date**: 2026-02-14 18:30 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 \
+    NationalLibraryOfScotland/medical-history-of-british-india \
+    <output-dataset> \
+    --image-column image \
+    --batch-size 16
+```
+
+## Performance
+
+- **Processing Speed**: ~0.04 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)