  Shadows in scanned documents pose significant challenges for document
analysis and recognition tasks due to their negative impact on visual quality
and readability. Current shadow removal techniques, including traditional
methods and deep learning approaches, face limitations in handling varying
shadow intensities and preserving document details. To address these issues, we
propose DocDeshadower, a novel multi-frequency Transformer-based model built
upon the Laplacian Pyramid. By decomposing the shadow image into multiple
frequency bands and employing two critical modules: the Attention-Aggregation
Network for low-frequency shadow removal and the Gated Multi-scale Fusion
Transformer for global refinement. DocDeshadower effectively removes shadows at
different scales while preserving document content. Extensive experiments
demonstrate DocDeshadower's superior performance compared to state-of-the-art
methods, highlighting its potential to significantly improve document shadow
removal techniques. The code is available at
https://github.com/leiyingtie/DocDeshadower.
