Generative adversarial networks for rubber stamp extraction and removal

Abstract

Due to the variety of documents and people's different stamping habits, it is difficult for current models to achieve satisfactory results in the process of extracting and removing rubber stamps. Most current methods cannot extract or remove rubber stamps without destroying the background text information. To solve the above problems, we propose a neural network that uses a double-cycle structure, named stamp extraction and removal generative adversarial network (SERGAN). In detail, we improve SERGAN's cycle-consistency by simultaneously recombining the generated rubber stamp image and unstamped image into a stamped image, which enables SERGAN to have multi-domain image conversion capability. To prove the model's generalization ability, we compare it with current mainstream methods on the public watermark dataset CLWD, and the experimental results show the excellence of our model. In addition, there is no publicly available dataset for rubber stamp extraction and removal experiments. To address the scarcity of rubber stamp datasets, we construct a large-scale rubber stamp dataset named LRSD.