andesco/risk-management-resources-ocr-sample

Document OCR using DeepSeek-OCR

This dataset contains markdown-formatted OCR results from images in andesco/risk-management-resources-images-sample using DeepSeek-OCR.

Processing Details

• —Source Dataset: andesco/risk-management-resources-images-sample
• —Model: deepseek-ai/DeepSeek-OCR
• —Number of Samples: 10
• —Processing Time: 1.5 min
• —Processing Date: 2026-02-21 19:32 UTC

Configuration

• —Image Column: image
• —Output Column: markdown
• —Dataset Split: train
• —Batch Size: 8
• —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

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 \\
    andesco/risk-management-resources-images-sample \\
    <output-dataset> \\
    --image-column image

Performance

• —Processing Speed: ~0.1 images/second
• —Processing Method: Batch processing with vLLM (2-3x speedup over sequential)

Generated with UV Scripts