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Robust offline handwritten character recognition through exploring writer-independent features under the guidance of printed data
Zhang, Yaping1,2; Liang, Shan1; Nie, Shuai1,2; Liu, Wenju1; Peng, Shouye3
Source PublicationPATTERN RECOGNITION LETTERS
2018-04-15
Volume106Pages:20-26
SubtypeArticle
AbstractDeep convolutional neural networks have made great progress in recent handwritten character recognition (HCR) by learning discriminative features from large amounts of labeled data. However, the large variance of handwriting styles across writers is still a big challenge to the robust HCR. To alleviate this issue, an intuitional idea is to extract writer-independent semantic features from handwritten characters, while standard printed characters are writer-independent stencils for handwritten characters. They could be used as prior knowledge to guide models to exploit writer-independent semantic features for HCR. In this paper, we propose a novel adversarial feature learning (AFL) model to incorporate the prior knowledge of printed data and writer-independent semantic features to improve the performance of HCR on limited training data. Different from available handcrafted features methods, the proposed AFL model exploits writer-independent semantic features automatically, and standard printed data as prior knowledge is learnt objectively. Systematic experiments on MNIST and CASIA-HWDB show that the proposed model is competitive with the state-of-the-art methods on the offline HCR task. (c) 2018 Elsevier B.V. All rights reserved.
KeywordHandwritten Character Recognition Writer-independent Features Adversarial Feature Learning Convolutional Neural Network
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.patrec.2018.02.006
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61573357 ; 61503382 ; 61403370 ; 61273267 ; 91120303)
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000429325500004
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22016
Collection模式识别国家重点实验室_机器人视觉
Affiliation1.Chinese Acad Sci, Inst Automat, Natl Lab Patten Recognit, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Xueersi Online Sch, Beijing, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Yaping,Liang, Shan,Nie, Shuai,et al. Robust offline handwritten character recognition through exploring writer-independent features under the guidance of printed data[J]. PATTERN RECOGNITION LETTERS,2018,106:20-26.
APA Zhang, Yaping,Liang, Shan,Nie, Shuai,Liu, Wenju,&Peng, Shouye.(2018).Robust offline handwritten character recognition through exploring writer-independent features under the guidance of printed data.PATTERN RECOGNITION LETTERS,106,20-26.
MLA Zhang, Yaping,et al."Robust offline handwritten character recognition through exploring writer-independent features under the guidance of printed data".PATTERN RECOGNITION LETTERS 106(2018):20-26.
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