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

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• 作者：Heng Zhang^a ; ^{heng.zhang@ia.ac.cn} ; Xiang-Dong Zhou^b ; ^{zhouxiangdong@cigit.ac.cn} ; Cheng-Lin Liu^c ; ^{liucl@nlpr.ia.ac.cn}
• 关键词：Online handwritten Chinese documents ; Semi-Markov conditional random fields ; Keyword spotting ; Proxy-character driven search
• 刊名：Engineering Applications of Artificial Intelligence
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：58
• 期：Complete
• 页码：49-61
• 全文大小：1932 K
• 卷排序：58

文摘

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.