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--- |
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tags: |
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- ocr |
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- document-processing |
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- glm-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 GLM-OCR |
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This dataset contains OCR results from images in [minhpvo/ocr-input](https://huggingface.co/datasets/minhpvo/ocr-input) using GLM-OCR, a compact 0.9B OCR model achieving SOTA performance. |
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## Processing Details |
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- **Source Dataset**: [minhpvo/ocr-input](https://huggingface.co/datasets/minhpvo/ocr-input) |
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- **Model**: [zai-org/GLM-OCR](https://huggingface.co/zai-org/GLM-OCR) |
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- **Task**: text recognition |
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- **Number of Samples**: 13 |
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- **Processing Time**: 2.3 min |
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- **Processing Date**: 2026-02-06 17:48 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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- **Max Model Length**: 8,192 tokens |
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- **Max Output Tokens**: 16,384 |
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- **Temperature**: 0.01 |
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- **Top P**: 1e-05 |
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- **GPU Memory Utilization**: 80.0% |
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## Model Information |
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GLM-OCR is a compact, high-performance OCR model: |
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- 0.9B parameters |
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- 94.62% on OmniDocBench V1.5 |
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- CogViT visual encoder + GLM-0.5B language decoder |
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- Multi-Token Prediction (MTP) loss for efficiency |
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- Multilingual: zh, en, fr, es, ru, de, ja, ko |
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- MIT licensed |
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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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## Reproduction |
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```bash |
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uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/glm-ocr.py \ |
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minhpvo/ocr-input \ |
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<output-dataset> \ |
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--image-column image \ |
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--batch-size 16 \ |
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--task ocr |
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``` |
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Generated with [UV Scripts](https://huggingface.co/uv-scripts) |
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