  Document shadow is a common issue that arises when capturing documents using
mobile devices, which significantly impacts readability. Current methods
encounter various challenges, including inaccurate detection of shadow masks
and estimation of illumination. In this paper, we propose ShaDocFormer, a
Transformer-based architecture that integrates traditional methodologies and
deep learning techniques to tackle the problem of document shadow removal. The
ShaDocFormer architecture comprises two components: the Shadow-attentive
Threshold Detector (STD) and the Cascaded Fusion Refiner (CFR). The STD module
employs a traditional thresholding technique and leverages the attention
mechanism of the Transformer to gather global information, thereby enabling
precise detection of shadow masks. The cascaded and aggregative structure of
the CFR module facilitates a coarse-to-fine restoration process for the entire
image. As a result, ShaDocFormer excels in accurately detecting and capturing
variations in both shadow and illumination, thereby enabling effective removal
of shadows. Extensive experiments demonstrate that ShaDocFormer outperforms
current state-of-the-art methods in both qualitative and quantitative
measurements.
