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--- |
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tags: |
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- ocr |
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- text-extraction |
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- rolmocr |
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- uv-script |
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- generated |
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--- |
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# OCR Text Extraction using RolmOCR |
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This dataset contains extracted text from images in [davanstrien/playbills-pdf-images-text](https://huggingface.co/datasets/davanstrien/playbills-pdf-images-text) using RolmOCR. |
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## Processing Details |
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- **Source Dataset**: [davanstrien/playbills-pdf-images-text](https://huggingface.co/datasets/davanstrien/playbills-pdf-images-text) |
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- **Model**: [reducto/RolmOCR](https://huggingface.co/reducto/RolmOCR) |
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- **Number of Samples**: 10 |
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- **Processing Time**: 5.8 minutes |
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- **Processing Date**: 2025-08-04 17:08 UTC |
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### Configuration |
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- **Image Column**: `image` |
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- **Output Column**: `rolmocr_text` |
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- **Dataset Split**: `train` |
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- **Batch Size**: 16 |
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- **Max Model Length**: 24,000 tokens |
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- **Max Output Tokens**: 16,000 |
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- **GPU Memory Utilization**: 80.0% |
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## Model Information |
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RolmOCR is a fast, general-purpose OCR model based on Qwen2.5-VL-7B architecture. It extracts plain text from document images with high accuracy and efficiency. |
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## Dataset Structure |
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The dataset contains all original columns plus: |
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- `rolmocr_text`: The extracted text from each image |
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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 extracted text |
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for example in dataset: |
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print(example["rolmocr_text"]) |
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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) RolmOCR script: |
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```bash |
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uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/rolm-ocr.py \ |
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davanstrien/playbills-pdf-images-text \ |
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<output-dataset> \ |
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--image-column image \ |
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--batch-size 16 \ |
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--max-model-len 24000 \ |
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--max-tokens 16000 \ |
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--gpu-memory-utilization 0.8 |
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``` |
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## Performance |
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- **Processing Speed**: ~0.0 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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