CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Dense Chained Attention Network for Scene Text Recognition
Gao, Yunze1,2; Chen, Yingying1,2; Wang, Jinqiao1,2; Tang, Ming1,2; Lu, Hanqing1,2
2018-10
Conference NameInternational Conference on Image Processing
Conference Date2018-10
Conference PlaceAthens,Greece
Abstract

Reading text in the wild is a challenging task in computer vision. Scene text suffers from various background noise, including shadow, irrelevant symbols and background texture. In order to reduce the disturbance of background noise, we propose a dense chained attention network with stacked attention modules for scene text recognition. Each attention module learns the attention map that is adapted to corresponding features to enhance the foreground text and suppress the background noise. Besides, the attention branch is designed with the convolution-deconvolution structure which rapidly captures global information to guide the discriminative feature selection. We stack multiple attention modules to gradually refine the attention maps and capture both the low-level appearance feature and the high-level semantic information. Extensive experiments on the standard benchmarks, the Street View Text, IIIT5K, and ICDAR datasets validate the superiority of the proposed method. The dense chained attention network achieves state-of-the-art or highly competitive recognition performance.

Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/39293
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorGao, Yunze
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Gao, Yunze,Chen, Yingying,Wang, Jinqiao,et al. Dense Chained Attention Network for Scene Text Recognition[C],2018.
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