Scene Text Removal Based on Multi-scale Attention Mechanism
HE Ping1, ZHANG Heng2, LIU Chenglin2,3
1.School of Computer Science and Technology, Anhui University, Hefei 230601; 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190; 3.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049
Abstract：Scene text removal is of great significance for privacy protection and image editing in image communication. However, existing scene text removal models are insufficient in extracting robust features for images with complex background and multi-scale texts, resulting in incomplete text detection and background repair. To solve this problem, a scene text removal framework based on multi-scale attention mechanism is proposed for robust background repair and text detection. The proposed framework is mainly composed of background repair network and text detection network, sharing a backbone network. In the background repair network, a texture adaptive module is designed to encode the channel/spatial features and adaptively integrate local/global features, effectively repairing shadow parts in text reconstruction. To improve text detection, a context aware module is designed to learn the discriminative features between texts and non-texts in the image. Besides, to enhance the receptive field of the network and improve the removal of multi-scale texts, a multi-scale feature loss function is designed to optimize the background repair and text detection modules. Experimental results on SCUT-SYN and SCUT-EnsText datasets show that the proposed method can achieve the state-of-the-art performance in text removal.
何平, 张恒, 刘成林. 基于多尺度注意力机制的场景文本擦除[J]. 模式识别与人工智能, 2022, 35(7): 614-624.
HE Ping, ZHANG Heng, LIU Chenglin. Scene Text Removal Based on Multi-scale Attention Mechanism. Pattern Recognition and Artificial Intelligence, 2022, 35(7): 614-624.
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2022, Vol. 35 
