CASIA OpenIR  > 模式识别国家重点实验室  > 模式分析与学习
Keyword spotting in handwritten chinese documents using semi-markov conditional random fields
Zhang, Heng1; Zhou, Xiang-Dong2; Liu, Cheng-Lin3
Source PublicationENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
2017-02-01
Volume58Pages:49-61
SubtypeArticle
AbstractThis paper proposes a document indexing method for keyword spotting based on semi-Markov conditional random fields (semi-CRFs), which provide a theoretical framework for fusing the information of different contexts. The candidate segmentation-recognition lattice is first augmented based on the linguistic context to improve recognition results. For fast retrieval and to save storage space, the lattice is then purged by a forward backward pruning procedure. In the reduced lattice, we estimate character similarity scores based on the semi-CRF model. The parameters of semi-CRF model are estimated using a binary classification objective, i.e., the cross-entropy (CE) to discriminate candidate characters in the lattice. To locate mis-recognized character instances in the lattice, we use confusing similar characters as proxies and search for proxy-characters in the index file. The proxy-character driven search can significantly improve the performance compared with our previous character-synchronous dynamic search (CSDS) method. Experimental results on the online handwriting database CASIA-OLHWDB justify the effectiveness of the proposed method.
KeywordOnline Handwritten Chinese Documents Semi-markov Conditional Random Fields Keyword Spotting Proxy-character Driven Search
WOS HeadingsScience & Technology ; Technology
DOI10.1016/j.engappai.2016.11.006
WOS KeywordCONTINUOUS SPEECH RECOGNITION ; CHARACTER-RECOGNITION ; TEXT RECOGNITION ; ONLINE ; MODEL ; SEGMENTATION ; RETRIEVAL
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China (NSFC)(61403385 ; 61273269)
WOS Research AreaAutomation & Control Systems ; Computer Science ; Engineering
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic
WOS IDWOS:000392684200004
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14365
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, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Intelligent Media Tech Res Ctr, Chongqing 400714, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhang, Heng,Zhou, Xiang-Dong,Liu, Cheng-Lin. Keyword spotting in handwritten chinese documents using semi-markov conditional random fields[J]. ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,2017,58:49-61.
APA Zhang, Heng,Zhou, Xiang-Dong,&Liu, Cheng-Lin.(2017).Keyword spotting in handwritten chinese documents using semi-markov conditional random fields.ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE,58,49-61.
MLA Zhang, Heng,et al."Keyword spotting in handwritten chinese documents using semi-markov conditional random fields".ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 58(2017):49-61.
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