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--- |
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tags: |
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- ocr |
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- document-processing |
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- lighton-ocr |
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- markdown |
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- uv-script |
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- generated |
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--- |
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# Document OCR using LightOnOCR-0.9B-32k-1025 |
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This dataset contains OCR results from images in [stckmn/ocr-input-Directive017-1761353279](https://huggingface.co/datasets/stckmn/ocr-input-Directive017-1761353279) using LightOnOCR, a fast and compact 1B OCR model. |
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## Processing Details |
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- **Source Dataset**: [stckmn/ocr-input-Directive017-1761353279](https://huggingface.co/datasets/stckmn/ocr-input-Directive017-1761353279) |
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- **Model**: [lightonai/LightOnOCR-0.9B-32k-1025](https://huggingface.co/lightonai/LightOnOCR-0.9B-32k-1025) |
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- **Vocabulary Size**: 32k tokens |
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- **Number of Samples**: 21 |
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- **Processing Time**: 1.2 min |
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- **Processing Date**: 2025-10-25 00:50 UTC |
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### Configuration |
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- **Image Column**: `image` |
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- **Output Column**: `markdown` |
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- **Dataset Split**: `train` |
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- **Batch Size**: 32 |
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- **Target Image Size**: 1288px (longest dimension) |
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- **Max Model Length**: 8,192 tokens |
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- **Max Output Tokens**: 6,500 |
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- **Temperature**: 0.2 |
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- **Top P**: 0.9 |
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- **GPU Memory Utilization**: 80.0% |
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## Model Information |
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LightOnOCR is a fast, compact OCR model that excels at: |
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- ⚡ **Production Speed** - 5.71 pages/second on H100 GPU |
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- 🎯 **Compact Size** - Only 1B parameters |
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- 📐 **LaTeX formulas** - Mathematical notation in LaTeX format |
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- 📊 **Tables** - Extracted and formatted as markdown |
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- 📝 **Document structure** - Hierarchy and layout preservation |
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- 🌍 **Multilingual** - Optimized for European languages |
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- 🔤 **Flexible vocabulary** - 151k/32k/16k token variants |
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### Vocabulary Variants |
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- **151k tokens**: Full vocabulary, supports all languages |
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- **32k tokens**: European languages optimized (~12% faster decoding) |
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- **16k tokens**: European languages optimized (~12% faster decoding) |
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## Dataset Structure |
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The dataset contains all original columns plus: |
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- `markdown`: The extracted text in markdown format with LaTeX formulas |
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- `inference_info`: JSON list tracking all OCR models applied to this dataset |
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## Usage |
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```python |
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from datasets import load_dataset |
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import json |
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# Load the dataset |
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dataset = load_dataset("{output_dataset_id}", split="train") |
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# Access the markdown text |
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for example in dataset: |
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print(example["markdown"]) |
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break |
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# View all OCR models applied to this dataset |
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inference_info = json.loads(dataset[0]["inference_info"]) |
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for info in inference_info: |
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print(f"Column: {info['column_name']} - Model: {info['model_id']}") |
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``` |
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## Reproduction |
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This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) LightOnOCR script: |
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```bash |
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uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/lighton-ocr.py \ |
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stckmn/ocr-input-Directive017-1761353279 \ |
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<output-dataset> \ |
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--vocab-size 32k \ |
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--image-column image \ |
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--batch-size 32 |
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``` |
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## Performance |
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- **Processing Speed**: ~0.29 images/second |
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- **Benchmark Score**: 76.1% overall (across diverse document types) |
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- **Optimization**: Native resolution ViT + lightweight decoder |
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Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts) |
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