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--- |
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viewer: false |
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tags: |
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- ocr |
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- document-processing |
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- dots-ocr |
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- multilingual |
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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 dots.ocr |
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This dataset contains OCR results from images in [NationalLibraryOfScotland/Scottish-School-Exam-Papers](https://huggingface.co/datasets/NationalLibraryOfScotland/Scottish-School-Exam-Papers) using DoTS.ocr, a compact 1.7B multilingual model. |
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## Processing Details |
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- **Source Dataset**: [NationalLibraryOfScotland/Scottish-School-Exam-Papers](https://huggingface.co/datasets/NationalLibraryOfScotland/Scottish-School-Exam-Papers) |
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- **Model**: [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr) |
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- **Number of Samples**: 10 |
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- **Processing Time**: 1.6 min |
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- **Processing Date**: 2025-10-07 14:23 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**: 16 |
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- **Prompt Mode**: layout-all |
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- **Max Model Length**: 8,192 tokens |
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- **Max Output Tokens**: 8,192 |
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- **GPU Memory Utilization**: 80.0% |
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## Model Information |
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DoTS.ocr is a compact multilingual document parsing model that excels at: |
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- 🌍 **100+ Languages** - Multilingual document support |
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- 📊 **Table extraction** - Structured data recognition |
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- 📐 **Formulas** - Mathematical notation preservation |
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- 📝 **Layout-aware** - Reading order and structure preservation |
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- ⚡ **Fast inference** - 2-3x faster than native HF with vLLM |
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- 🎯 **Compact** - Only 1.7B parameters |
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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 |
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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) DoTS OCR script: |
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```bash |
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uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/dots-ocr.py \ |
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NationalLibraryOfScotland/Scottish-School-Exam-Papers \ |
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<output-dataset> \ |
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--image-column image \ |
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--batch-size 16 \ |
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--prompt-mode layout-all \ |
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--max-model-len 8192 \ |
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--max-tokens 8192 \ |
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--gpu-memory-utilization 0.8 |
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``` |
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## Performance |
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- **Processing Speed**: ~0.1 images/second |
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- **GPU Configuration**: vLLM with 80% GPU memory utilization |
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Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts) |
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