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Document shadow is a common issue that arise when capturing documents usingmobile devices, which significantly impacts the readability. Current methodsencounter various challenges including inaccurate detection of shadow masks andestimation of illumination. In this paper, we propose ShaDocFormer, aTransformer-based architecture that integrates traditional methodologies anddeep learning techniques to tackle the problem of document shadow removal. TheShaDocFormer architecture comprises two components: the Shadow-attentiveThreshold Detector (STD) and the Cascaded Fusion Refiner (CFR). The STD moduleemploys a traditional thresholding technique and leverages the attentionmechanism of the Transformer to gather global information, thereby enablingprecise detection of shadow masks. The cascaded and aggregative structure ofthe CFR module facilitates a coarse-to-fine restoration process for the entireimage. As a result, ShaDocFormer excels in accurately detecting and capturingvariations in both shadow and illumination, thereby enabling effective removalof shadows. Extensive experiments demonstrate that ShaDocFormer outperformscurrent state-of-the-art methods in both qualitative and quantitativemeasurements.

ShaDocFormer: A Shadow-attentive Threshold Detector with Cascaded Fusion Refiner for document shadow removal