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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
2018-04-15
发表期刊PATTERN RECOGNITION LETTERS
卷号106页码:20-26
文章类型Article
摘要Deep 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.
关键词Handwritten Character Recognition Writer-independent Features Adversarial Feature Learning Convolutional Neural Network
WOS标题词Science & Technology ; Technology
DOI10.1016/j.patrec.2018.02.006
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61573357 ; 61503382 ; 61403370 ; 61273267 ; 91120303)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000429325500004
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/22016
专题模式识别国家重点实验室_机器人视觉
作者单位1.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
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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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