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Encyclopaedia Britannica
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Smellie, William
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EB.1
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End of preview. Expand in Data Studio

Document OCR using DeepSeek-OCR

This dataset contains markdown-formatted OCR results from images in davanstrien/ency-test using DeepSeek-OCR.

Processing Details

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

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 DeepSeek OCR vLLM script:

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

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