  Shadow removal improves the visual quality and legibility of digital copies
of documents. However, document shadow removal remains an unresolved subject.
Traditional techniques rely on heuristics that vary from situation to
situation. Given the quality and quantity of current public datasets, the
majority of neural network models are ill-equipped for this task. In this
paper, we propose a Transformer-based model for document shadow removal that
utilizes shadow context encoding and decoding in both shadow and shadow-free
regions. Additionally, shadow detection and pixel-level enhancement are
included in the whole coarse-to-fine process. On the basis of comprehensive
benchmark evaluations, it is competitive with state-of-the-art methods.
