Robust offline handwritten character recognition through exploring writer-independent features under the guidance of printed data

doi:10.1016/j.patrec.2018.02.006

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 机器人视觉团队

	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, Shouye 3
刊名	PATTERN RECOGNITION LETTERS
	2018-04-15
卷号	106 页码:20-26
关键词	Handwritten Character Recognition Writer-independent Features Adversarial Feature Learning Convolutional Neural Network
DOI	10.1016/j.patrec.2018.02.006
文献子类	Article
英文摘要	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.
WOS研究方向	Computer Science
语种	英语
WOS记录号	WOS:000429325500004
资助机构	National Natural Science Foundation of China(61573357 ; 61503382 ; 61403370 ; 61273267 ; 91120303)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/22016]
专题	自动化研究所_模式识别国家重点实验室_机器人视觉团队
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
推荐引用方式 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.