README.md

---
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 [davanstrien/ency-test](https://huggingface.co/datasets/davanstrien/ency-test) using DeepSeek-OCR.

## Processing Details

- **Source Dataset**: [davanstrien/ency-test](https://huggingface.co/datasets/davanstrien/ency-test)
- **Model**: [deepseek-ai/DeepSeek-OCR](https://huggingface.co/deepseek-ai/DeepSeek-OCR)
- **Number of Samples**: 100
- **Processing Time**: 8.5 min
- **Processing Date**: 2025-10-22 17:48 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 \\
    davanstrien/ency-test \\
    <output-dataset> \\
    --resolution-mode large \\
    --image-column image
```

## Performance

- **Processing Speed**: ~0.2 images/second
- **Processing Method**: Batch processing with vLLM (2-3x speedup over sequential)

Generated with 🤖 [UV Scripts](https://huggingface.co/uv-scripts)