A Deep Learning Method for Document Shadow Removal with Sobel Prior under Mask Supervision

Abstract When digitizing documents using conventional equipment, shadows often appear, posing significant challenges to the visual quality and readability of the digital copies. Given that the removal of document shadows typically involves complex image processing and computational tasks, which require substantial computational resources and time, the cost can become prohibitive, limiting the practicality and efficiency of shadow removal algorithms. This research aims to address the critical task of designing a model capable of achieving superior shadow removal effects. We propose a deep learning model for document shadow removal that harnesses Sobel text prior and ground truth masks as supervision. This prior knowledge encapsulates regular information regarding document structure and shadow formation, thereby enhancing its ability to utilize edge information for shadow removal. Additionally, the integration of prior knowledge and supervised learning can help the model learn more quickly, reducing the amount of information the model needs to process and improving its efficiency.