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---
tags:
- ocr
- document-processing
- deepseek
- deepseek-ocr
- markdown
- uv-script
- generated
---
# Document OCR using DeepSeek-OCR
This dataset contains markdown-formatted OCR results from images in [NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset](https://huggingface.co/datasets/NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset) using DeepSeek-OCR.
## Processing Details
- **Source Dataset**: [NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset](https://huggingface.co/datasets/NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset)
- **Model**: [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR)
- **Number of Samples**: 100
- **Processing Time**: 3.0 min
- **Processing Date**: 2025-10-22 18:00 UTC
### Configuration
- **Image Column**: `image`
- **Output Column**: `markdown`
- **Dataset Split**: `train`
- **Batch Size**: 512
- **Resolution Mode**: large
- **Base Size**: 1280
- **Image Size**: 1280
- **Crop Mode**: False
- **Max Model Length**: 8,192 tokens
- **Max Output Tokens**: 8,192
- **GPU Memory Utilization**: 80.0%
## Model Information
DeepSeek-OCR is a state-of-the-art document OCR model that excels at:
- 📐 **LaTeX equations** - Mathematical formulas preserved in LaTeX format
- 📊 **Tables** - Extracted and formatted as HTML/markdown
- 📝 **Document structure** - Headers, lists, and formatting maintained
- 🖼️ **Image grounding** - Spatial layout and bounding box information
- 🔍 **Complex layouts** - Multi-column and hierarchical structures
- 🌍 **Multilingual** - Supports multiple languages
### Resolution Modes
- **Tiny** (512×512): Fast processing, 64 vision tokens
- **Small** (640×640): Balanced speed/quality, 100 vision tokens
- **Base** (1024×1024): High quality, 256 vision tokens
- **Large** (1280×1280): Maximum quality, 400 vision tokens
- **Gundam** (dynamic): Adaptive multi-tile processing for large documents
## Dataset Structure
The dataset contains all original columns plus:
- `markdown`: The extracted text in markdown format with preserved structure
- `inference_info`: JSON list tracking all OCR models applied to this dataset
## Usage
```python
from datasets import load_dataset
import json
# Load the dataset
dataset = load_dataset("{{output_dataset_id}}", split="train")
# Access the markdown text
for example in dataset:
print(example["markdown"])
break
# View all OCR models applied to this dataset
inference_info = json.loads(dataset[0]["inference_info"])
for info in inference_info:
print(f"Column: {{info['column_name']}} - Model: {{info['model_id']}}")
```
## Reproduction
This dataset was generated using the [uv-scripts/ocr](https://huggingface.co/datasets/uv-scripts/ocr) DeepSeek OCR vLLM script:
```bash
uv run https://huggingface.co/datasets/uv-scripts/ocr/raw/main/deepseek-ocr-vllm.py \\
NationalLibraryOfScotland/Britain-and-UK-Handbooks-Dataset \\
<output-dataset> \\
--resolution-mode large \\
--image-column image
```
## Performance
- **Processing Speed**: ~0.6 images/second
- **Processing Method**: Batch processing with vLLM (2-3x speedup over sequential)
Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)
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