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SSD-DIS: A Semi-Synthetic Shadow Dataset for Document Images.

Bingshu Wang1,2, Jia Li1, Ze Wang1

  • 1School of Software, Northwestern Polytechnical University, Xi'an, China.

Scientific Data
|May 2, 2026
PubMed
Summary

Researchers created a new dataset to improve shadow removal in document images captured by portable devices. This dataset helps AI models better detect and remove shadows, enhancing document readability for remote work and education.

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Area of Science:

  • Computer Vision
  • Image Processing
  • Machine Learning

Background:

  • Smartphones are increasingly used for digitizing physical documents, driven by remote work and education needs.
  • Shadows in captured document images significantly impair readability and information extraction.
  • Existing shadow removal datasets have limitations for training robust AI models.

Purpose of the Study:

  • To develop a novel semi-synthetic dataset (SSD-DIS) for improving document shadow removal.
  • To address the limitations of current datasets in simulating realistic shadow conditions.
  • To enhance the performance of AI models in document shadow removal tasks.

Main Methods:

  • A semi-synthetic dataset (SSD-DIS) comprising 12,224 image sets was created.

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  • Blender software was utilized to generate shadow masks and incorporate multi-source shadow-free images.
  • Shadow intensity and color were adjusted to simulate diverse real-world scenarios.

    • Main Results:

    • Experiments demonstrated that SSD-DIS effectively enhances neural networks' ability to learn document shadow features.
    • Models trained on SSD-DIS exhibited superior performance compared to those trained on traditional datasets.
    • The dataset supports advancements in research for document shadow removal algorithms.

    Conclusions:

    • The proposed SSD-DIS dataset is a valuable resource for advancing document shadow removal technology.
    • Training AI models on SSD-DIS leads to improved performance in handling shadows in document images.
    • This work contributes to better usability of digitized documents in remote contexts.