DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks
Authors: Jiaxin Zhang, Dezhi Peng, Chongyu Liu, Peirong Zhang, Lianwen Jin

Document image restoration is a crucial aspect of Document AI systems as the quality of document images significantly influences the overall performance. Prevailing methods address distinct restoration tasks independently leading to intricate systems and the incapability to harness the potential synergies of multi-task learning. To overcome this challenge we propose DocRes a generalist model that unifies five document image restoration tasks including dewarping deshadowing appearance enhancement deblurring and binarization. To instruct DocRes to perform various restoration tasks we propose a novel visual prompt approach called Dynamic Task-Specific Prompt (DTSPrompt). The DTSPrompt for different tasks comprises distinct prior features which are additional characteristics extracted from the input image. Beyond its role as a cue for task-specific execution DTSPrompt can also serve as supplementary information to enhance the model's performance. Moreover DTSPrompt is more flexible than prior visual prompt approaches as it can be seamlessly applied and adapted to inputs with high and variable resolutions. Experimental results demonstrate that DocRes achieves competitive or superior performance compared to existing state-of-the-art task-specific models. This underscores the potential of DocRes across a broader spectrum of document image restoration tasks. The source code is publicly available at https://github.com/ZZZHANG-jx/DocRes.