Mask-Guided Stamp Erasure for Real Document Image

Xinye Yang, Dongbao Yang, Yu Zhou, Youhui Guo, Weiping Wang

Abstract: The application of text recognition in the automatic analysis of invoices, contracts and other documents has significantly raised office efficiency, but the stamps overlapping with the texts in these documents may seriously degrade the recognition accuracy. To mitigate the negative effect, we propose a stamp eraser, which can simultaneously remove the stamps and recover the occluded texts. To better distinguish stamps from the complex background, we propose a stamp localization module to generate fine-grained binary masks for the eraser to focus more on stamps. This module can also provide the background textual information for recovering text with a skip connection. We also propose the dilated mask to make the generated image look more natural by filling the stamp area with pixels around the stamp. In addition, to evaluate the effectiveness of our method in boosting text recognition, we propose a synthetic data set for training and a real dataset with complex background for testing. Experiments have shown that our method can effectively improve the recognition accuracy of the text occluded with the stamps.