CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Drawing and Recognizing Chinese Characters with Recurrent Neural Network
Zhang, Xu-Yao1; Yin, Fei1; Zhang, Yan-Ming1; Liu, Cheng-Lin1,2; Bengio, Yoshua3
Source PublicationIEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
2018-04-01
Volume40Issue:4Pages:849-862
SubtypeArticle
AbstractRecent deep learning based approaches have achieved great success on handwriting recognition. Chinese characters are among the most widely adopted writing systems in the world. Previous research has mainly focused on recognizing handwritten Chinese characters. However, recognition is only one aspect for understanding a language, another challenging and interesting task is to teach a machine to automatically write (pictographic) Chinese characters. In this paper, we propose a framework by using the recurrent neural network (RNN) as both a discriminative model for recognizing Chinese characters and a generative model for drawing (generating) Chinese characters. To recognize Chinese characters, previous methods usually adopt the convolutional neural network (CNN) models which require transforming the online handwriting trajectory into image-like representations. Instead, our RNN based approach is an end-to-end system which directly deals with the sequential structure and does not require any domain-specific knowledge. With the RNN system (combining an LSTM and GRU), state-of-the-art performance can be achieved on the ICDAR-2013 competition database. Furthermore, under the RNN framework, a conditional generative model with character embedding is proposed for automatically drawing recognizable Chinese characters. The generated characters (in vector format) are human-readable and also can be recognized by the discriminative RNN model with high accuracy. Experimental results verify the effectiveness of using RNNs as both generative and discriminative models for the tasks of drawing and recognizing Chinese characters.
KeywordRecurrent Neural Network Lstm Gru Discriminative Model Generative Model Handwriting
WOS HeadingsScience & Technology ; Technology
DOI10.1109/TPAMI.2017.2695539
WOS KeywordHANDWRITING RECOGNITION COMPETITION ; OF-THE-ART ; ONLINE ; DATABASES ; ORDER
Indexed BySCI
Language英语
Funding OrganizationStrategic Priority Research Program of the Chinese Academy of Sciences(XDB02060009) ; National Natural Science Foundation of China(61403380 ; 61573355)
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000426687100006
Citation statistics
Cited Times:22[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15357
Collection模式识别国家重点实验室_模式分析与学习
Affiliation1.Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100049, Peoples R China
3.Univ Montreal, MILA Lab, Montreal, PQ H3T 1J4, Canada
Recommended Citation
GB/T 7714
Zhang, Xu-Yao,Yin, Fei,Zhang, Yan-Ming,et al. Drawing and Recognizing Chinese Characters with Recurrent Neural Network[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2018,40(4):849-862.
APA Zhang, Xu-Yao,Yin, Fei,Zhang, Yan-Ming,Liu, Cheng-Lin,&Bengio, Yoshua.(2018).Drawing and Recognizing Chinese Characters with Recurrent Neural Network.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,40(4),849-862.
MLA Zhang, Xu-Yao,et al."Drawing and Recognizing Chinese Characters with Recurrent Neural Network".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 40.4(2018):849-862.
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