Keyword spotting in unconstrained handwritten Chinese documents using contextual word model

  • Authors:
  • Liang Huang;Fei Yin;Qing-Hu Chen;Cheng-Lin Liu

  • Affiliations:
  • School of Electronic Information, Wuhan University, 39 Luoyu Road, Wuhan, Hubei 430079, PR China;National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, PR China;School of Electronic Information, Wuhan University, 39 Luoyu Road, Wuhan, Hubei 430079, PR China;National Laboratory of Pattern Recognition (NLPR), Institute of Automation of Chinese Academy of Sciences, 95 Zhongguancun East Road, Beijing 100190, PR China

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2013

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Abstract

This paper proposes a method for keyword spotting in off-line Chinese handwritten documents using a contextual word model, which measures the similarity between the query word and every candidate word in the document by combining a character classifier and the geometric context as well as linguistic context. The geometric context model characterizes the single-character likeliness and between-character relationship. The linguistic model utilizes the dependency of the word with the external adjacent characters. The combining weights are optimized on training documents. Experiments on a large handwriting database CASIA-HWDB demonstrate the effectiveness of the proposed method and justify the benefits of geometric and linguistic contexts. Compared to transcription-based text search, the proposed method can provide higher recall rate, and for spotting words of four characters, the proposed method provides both higher precision and recall rate.