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--- |
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tags: |
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- ocr |
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- document-processing |
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- deepseek |
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- deepseek-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 DeepSeek-OCR |
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This dataset contains markdown-formatted OCR results from images in [Alysonhower/test](https://huggingface.co/datasets/Alysonhower/test) using DeepSeek-OCR. |
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## Processing Details |
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- **Source Dataset**: [Alysonhower/test](https://huggingface.co/datasets/Alysonhower/test) |
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- **Model**: [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR) |
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- **Number of Samples**: 1 |
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- **Processing Time**: 1.5 minutes |
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- **Processing Date**: 2025-10-23 13:06 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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- **Resolution Mode**: gundam |
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- **Base Size**: 1024 |
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- **Image Size**: 640 |
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- **Crop Mode**: True |
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## Model Information |
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DeepSeek-OCR is a state-of-the-art document OCR model that excels at: |
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- 📐 **LaTeX equations** - Mathematical formulas preserved in LaTeX format |
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- 📊 **Tables** - Extracted and formatted as HTML/markdown |
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- 📝 **Document structure** - Headers, lists, and formatting maintained |
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- 🖼️ **Image grounding** - Spatial layout and bounding box information |
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- 🔍 **Complex layouts** - Multi-column and hierarchical structures |
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- 🌍 **Multilingual** - Supports multiple languages |
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### Resolution Modes |
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- **Tiny** (512×512): Fast processing, 64 vision tokens |
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- **Small** (640×640): Balanced speed/quality, 100 vision tokens |
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- **Base** (1024×1024): High quality, 256 vision tokens |
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- **Large** (1280×1280): Maximum quality, 400 vision tokens |
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- **Gundam** (dynamic): Adaptive multi-tile processing for large documents |
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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 preserved structure |
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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) DeepSeek OCR script: |
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```bash |
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uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr.py \ |
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Alysonhower/test \ |
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<output-dataset> \ |
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--resolution-mode gundam \ |
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--image-column image |
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``` |
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## Performance |
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- **Processing Speed**: ~0.0 images/second |
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- **Processing Method**: Sequential (Transformers API, no batching) |
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Note: This uses the official Transformers implementation. For faster batch processing, |
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consider using the vLLM version once DeepSeek-OCR is officially supported by vLLM. |
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Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts) |
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