End-to-End Online Writer Identification With Recurrent Neural Network
Zhang, Xu-Yao1; Xie, Guo-Sen1; Liu, Cheng-Lin1,2,3; Bengio, Yoshua4
2017-04-01
发表期刊IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
卷号47期号:2页码:285-292
文章类型Article
摘要Writer identification is an important topic for pattern recognition and artificial intelligence. Traditional methods rely heavily on sophisticated hand-crafted features to represent the characteristics of different writers. In this paper, we propose an end-to-end framework for online text-independent writer identification by using a recurrent neural network (RNN). Specifically, the handwriting data of a particular writer are represented by a set of random hybrid strokes (RHSs). Each RHS is a randomly sampled short sequence representing pen tip movements (xy-coordinates) and pen-down or pen-up states. RHS is independent of the content and language involved in handwriting; therefore, writer identification at the RHS level is more general and convenient than the character level or the word level, which also requires character/word segmentation. The RNN model with bidirectional long short-term memory is used to encode each RHS into a fixed-length vector for final classification. All the RHSs of a writer are classified independently, and then, the posterior probabilities are averaged to make the final decision. The proposed framework is end-to-end and does not require any domain knowledge for handwriting data analysis. Experiments on both English (133 writers) and Chinese (186 writers) databases verify the advantages of our method compared with other state-of-the-art approaches.
关键词End-to-end Long Short-term Memory (Lstm) Online Handwriting Recurrent Neural Network (Rnn) Writer Identification
WOS标题词Science & Technology ; Technology
DOI10.1109/THMS.2016.2634921
关键词[WOS]SIGNATURE VERIFICATION ; REPRESENTATION ; RECOGNITION ; SYSTEM ; LSTM
收录类别SCI
语种英语
项目资助者Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02060009) ; National Natural Science Foundation of China(61403380)
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号WOS:000396401600011
引用统计
被引频次:9[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/14390
专题模式识别国家重点实验室_模式分析与学习
作者单位1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China
2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
4.Univ Montreal, Montreal Inst Learning Algorithms, Montreal, PQ H3T 1J4, Canada
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GB/T 7714
Zhang, Xu-Yao,Xie, Guo-Sen,Liu, Cheng-Lin,et al. End-to-End Online Writer Identification With Recurrent Neural Network[J]. IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS,2017,47(2):285-292.
APA Zhang, Xu-Yao,Xie, Guo-Sen,Liu, Cheng-Lin,&Bengio, Yoshua.(2017).End-to-End Online Writer Identification With Recurrent Neural Network.IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS,47(2),285-292.
MLA Zhang, Xu-Yao,et al."End-to-End Online Writer Identification With Recurrent Neural Network".IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 47.2(2017):285-292.
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