Dataset Viewer
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				 stringlengths 9 9 | page_number
				 int32 1 879 | file_identifier
				 stringlengths 9 9 | image
				 imagewidth (px) 600 600 | text
				 stringlengths 0 8.94k | alto_xml
				 stringlengths 714 233k | has_image
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				values | volume_part
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				 stringclasses 5
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				 stringclasses 14
				values | markdown
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	Encyclopaedia Britannica - Third edition, Volume 18, STR-ZYM - EB.5 | true | 
	Third edition | 
	Third edition, Volume 18, STR-ZYM | 
	1797 | 
	Encyclopaedia Britannica | 
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	Second edition | 
	Second edition, Volume 6, K-Medicine | 
	1778-83 | 
	Encyclopaedia Britannica | 
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	Second edition | 
	Second edition, Volume 9, POI-SCU | 
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	Eighth edition, Volume 18, PLA-REI | 
	1853-1860 | 
	Stewart, Dugald | 
	Encyclopaedia Britannica | 
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	Third edition | 
	Third edition, Volume 6, DIA-ETH | 
	1797 | 
	Encyclopaedia Britannica | 
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	1824 | 
	Stewart, Dugald | 
	Supplement to the fourth, fifth and sixth editions of the Encyclopaedia Britannica | 
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	Encyclopaedia Britannica - Third edition, Volume 8, GOB-HYD - EB.5 | true | 
	Third edition | 
	Third edition, Volume 8, GOB-HYD | 
	1797 | 
	Encyclopaedia Britannica | 
	EB.5 | "466\n\nRules of Heraldry.\n\nOf the Rules or Laws of HERALDRY.\n\nTHE several cefutcheons, tinétur(...TRUNCATED) | "[{\"column_name\": \"markdown\", \"model_id\": \"deepseek-ai/DeepSeek-OCR\", \"processing_date\": \(...TRUNCATED) | |||
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	First edition | 
	First edition, 1771, Volume 3, M-Z | 
	1771 | 
	Smellie, William | 
	Encyclopaedia Britannica; or, A dictionary of arts and sciences, compiled upon a new plan | 
	EB.1 | "353\n\n**M U S**\n\nThe intervals of the notes of all sharp keys and flat keys repeatedly, are prop(...TRUNCATED) | "[{\"column_name\": \"markdown\", \"model_id\": \"deepseek-ai/DeepSeek-OCR\", \"processing_date\": \(...TRUNCATED) | 
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
- Source Dataset: davanstrien/ency-test
- Model: 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
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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