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

With rising demand for remote work and education, smartphones and other portable photographic devices are increasingly used to capture physical documents, which are then shared as electronic files. However, shadows in such images hinder reading. Currently available shadow removal datasets exhibit certain limitations. This paper creates a semi-synthetic dataset (SSD-DIS) with 12,224 image sets. Using Blender for shadow masks, multi-source shadow-free images, and adjusted shadow intensity/color, it simulates real-world shadow scenarios. Experiments show SSD-DIS enhances neural networks' learning of document shadow features; models trained on it outperform those using traditional datasets, supporting document shadow removal algorithm research.