CASIA OpenIR
Document image binarization with cascaded generators of conditional generative adversarial networks
Zhao, Jinyuan1,2; Shi, Cunzhao1; Jia, Fuxi1; Wang, Yanna1; Xiao, Baihua1
Source PublicationPATTERN RECOGNITION
ISSN0031-3203
2019-12-01
Volume96Pages:12
Corresponding AuthorShi, Cunzhao(cunzhao.shi@ia.ac.cn)
AbstractBinarization is often the first step in many document analysis tasks and plays a key role in the subsequent steps. In this paper, we formulate binarization as an image-to-image generation task and introduce the conditional generative adversarial networks (cGANs) to solve the core problem of multi-scale information combination in binarization task. Our generator consists of two stages: In the first stage, sub generator Cl learns to extract text pixels from an input image. Different scales of the input image are processed by G1 and corresponding binary images are generated. In the second stage, our sub-generator G2 learns a combination of results at different scales from the first stage and produces the final binary result. We conduct comprehensive experiments of the proposed method on nine public document image binarization datasets. Experimental results show that compared with many classical and state-of-the-art approaches, our method gains promising performance in the accuracy and robustness of binarization. (C) 2019 Elsevier Ltd. All rights reserved.
KeywordCascaded generator Conditional generative adversarial networks Document image binarization Image generation Historical document analysis
DOI10.1016/j.patcog.2019.106968
WOS KeywordCOMPETITION
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[71621002] ; Key Programs of the Chinese Academy of Sciences[ZDBS-SSW-JSC003] ; Key Programs of the Chinese Academy of Sciences[ZDBS-SSW-JSC004] ; Key Programs of the Chinese Academy of Sciences[ZDBS-SSW-JSC005]
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; Key Programs of the Chinese Academy of Sciences
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000487569700014
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26682
Collection中国科学院自动化研究所
Corresponding AuthorShi, Cunzhao
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhao, Jinyuan,Shi, Cunzhao,Jia, Fuxi,et al. Document image binarization with cascaded generators of conditional generative adversarial networks[J]. PATTERN RECOGNITION,2019,96:12.
APA Zhao, Jinyuan,Shi, Cunzhao,Jia, Fuxi,Wang, Yanna,&Xiao, Baihua.(2019).Document image binarization with cascaded generators of conditional generative adversarial networks.PATTERN RECOGNITION,96,12.
MLA Zhao, Jinyuan,et al."Document image binarization with cascaded generators of conditional generative adversarial networks".PATTERN RECOGNITION 96(2019):12.
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