Keyword spotting in handwritten chinese documents using semi-markov conditional random fields. (February 2017)

• Record Type: Journal Article
• Authors: Zhang, Heng; Zhou, Xiang-Dong; Liu, Cheng-Lin
• Is Part Of: Engineering applications of artificial intelligence. Volume 58(2016:Oct.)
• Page Start: 49
• Page End: 61
• Publication Date: 2017-02
• Subjects: Online handwritten Chinese documents -- Semi-Markov conditional random fields -- Keyword spotting -- Proxy-character driven search; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.11.006
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: 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.