DeSeal: Semantic-Aware Seal2Clear Attention for Document Seal Removal
Yifan Liu, Jiancheng Huang, Shifeng Chen · IEEE Signal Processing Letters · 2023
Seal removal aims to eliminate the seal portion from documents to facilitate better OCR and document reconstruction. However, existing seal removal methods often lack publicly available code and pre-trained models, and suffer from a lack of publicly available seal datasets. To address these issues, we propose DeSeal for seal removal and introduce a SealBank dataset containing 100K paired seal images. In DeSeal, we introduce the Semantic-Aware Seal2Clear Attention and Color-Adapter Module, where the former identifies seal regions in the entire image and focuses on removing seals from these areas, while the latter significantly improves the model's generalization performance, enabling it to perform well on both real and synthetic data. Experimental results on the SealBank dataset demonstrate the effectiveness of our proposed DeSeal.