CASIA OpenIR
Reading scene text with fully convolutional sequence modeling
Gao, Yunze1,2; Chen, Yingying1,2; Wang, Jinqiao1,2; Tang, Ming1,2; Lu, Hanqing1,2
Source PublicationNEUROCOMPUTING
ISSN0925-2312
2019-04-28
Volume339Pages:161-170
Corresponding AuthorChen, Yingying(yingying.chen@nlpr.ia.ac.cn)
AbstractReading text in the wild is a challenging task in computer vision. Existing approaches mainly adopt connectionist temporal classification (CTC) or attention models based on recurrent neural network (RNN), and are computationally expensive and hard to train. In this paper, instead of the chain structure of RNN, we propose an end-to-end fully convolutional network with the stacked convolutional layers to effectively capture the long-term dependencies among elements of scene text image. The stacked convolutional layers are much more efficient than bidirectional long short-term memory (BLSTM) in modeling the contextual dependency. In addition, we design a discriminative feature encoder by incorporating the residual attention blocks into a small densely connected network to enhance the foreground text and suppress the background noise. Extensive experiments on seven standard benchmarks, the Street View Text, IIIT5K, ICDAR03, ICDAR13, ICDAR15, COCO-Text and Total-Text, validate that our method not only achieves state-of-the-art or highly competitive recognition performance, but significantly improves the efficiency and reduces the number of parameters as well. (C) 2019 Elsevier B.V. All rights reserved.
KeywordFully convolutional sequence modeling Scene text recognition
DOI10.1016/j.neucom.2019.01.094
WOS KeywordRECOGNITION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61772527] ; National Natural Science Foundation of China[61806200]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000461166500016
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24985
Collection中国科学院自动化研究所
Corresponding AuthorChen, Yingying
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
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. Reading scene text with fully convolutional sequence modeling[J]. NEUROCOMPUTING,2019,339:161-170.
APA Gao, Yunze,Chen, Yingying,Wang, Jinqiao,Tang, Ming,&Lu, Hanqing.(2019).Reading scene text with fully convolutional sequence modeling.NEUROCOMPUTING,339,161-170.
MLA Gao, Yunze,et al."Reading scene text with fully convolutional sequence modeling".NEUROCOMPUTING 339(2019):161-170.
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