Keyword spotting in handwritten chinese documents using semi-markov conditional random fields

doi:10.1016/j.engappai.2016.11.006

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	Keyword spotting in handwritten chinese documents using semi-markov conditional random fields
	Zhang, Heng1 ; Zhou, Xiang-Dong 2; Liu, Cheng-Lin3
发表期刊	ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
	2017-02-01
卷号	58 页码:49-61
文章类型	Article
摘要	This 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.
关键词	Online Handwritten Chinese Documents Semi-markov Conditional Random Fields Keyword Spotting Proxy-character Driven Search
WOS标题词	Science & Technology ; Technology
DOI	10.1016/j.engappai.2016.11.006
关键词[WOS]	CONTINUOUS SPEECH RECOGNITION ; CHARACTER-RECOGNITION ; TEXT RECOGNITION ; ONLINE ; MODEL ; SEGMENTATION ; RETRIEVAL
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China (NSFC)(61403385 ; 61273269)
WOS研究方向	Automation & Control Systems ; Computer Science ; Engineering
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Engineering, Multidisciplinary ; Engineering, Electrical & Electronic
WOS记录号	WOS:000392684200004
引用统计	被引频次：4[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/173211/14365
专题	多模态人工智能系统全国重点实验室_模式分析与学习
作者单位	1.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
第一作者单位	中国科学院自动化研究所
推荐引用方式 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.