模式识别与人工智能
Saturday, March 15, 2025 Home      About Journal      Editorial Board      Instructions      Ethics Statement      Contact Us                   中文
Pattern Recognition and Artificial Intelligence  2022, Vol. 35 Issue (7): 614-624    DOI: 10.16451/j.cnki.issn1003-6059.202207004
Deep Learning Based Image Processing Current Issue| Next Issue| Archive| Adv Search |
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

Download: PDF (5454 KB)   HTML (1 KB) 
Export: BibTeX | EndNote (RIS)      
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.
Key wordsScene Text Erasure      Text Segmentation      Attention Mechanism      Multi-scale Features      End-to-End Method     
Received: 30 May 2022     
ZTFLH: TP 391  
Fund:Supported by National Natural Science Foundation of China(No.61936003,61721004)
Corresponding Authors: LIU Chenglin, Ph.D., professor. His research interests include pattern recognition, computer vision and document image analysis and recognition.   
About author:: About Author:HE Ping, master student. His research interests include scene text style transformation. ZHANG Heng, Ph.D., associate professor. His research interests include document image analysis and recognition.
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
HE Ping
ZHANG Heng
LIU Chenglin
Cite this article:   
HE Ping,ZHANG Heng,LIU Chenglin. Scene Text Removal Based on Multi-scale Attention Mechanism[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(7): 614-624.
URL:  
http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202207004      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2022/V35/I7/614
Copyright © 2010 Editorial Office of Pattern Recognition and Artificial Intelligence
Address: No.350 Shushanhu Road, Hefei, Anhui Province, P.R. China Tel: 0551-65591176 Fax:0551-65591176 Email: bjb@iim.ac.cn
Supported by Beijing Magtech  Email:support@magtech.com.cn