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收集所有交通大學研究人員與學生個人或團體所發表之各項學術著作，涵蓋期刊論文、教師專書、學位論文等項。;;Collections of Publications of NCTU Researchers and Students Like Journal Papers and Thesis.

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Subject: search.filters.subject.candidate-cluster selection

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    Speeding up Chinese character recognition in an automatic document reading system
    (1998-11-01)
    Tseng, YH
    ;
    Kuo, CC
    ;
    Lee, HJ
    ;
    資訊工程學系
    ;
    Department of Computer Science
    In this paper, we present two techniques for speeding up character recognition. Our character recognition system, including the candidate-cluster selection and modified branch-and-bound detail-matching modules, is implemented using two statistical features: crossing-counts and contour-direction counts. In the training stage, we divide characters into different clusters by using reference characters. To have a very high recognition rate, the candidate-cluster selection module selects the top 60 clusters with minimal distances from among 300 predefined clusters. To further speed-up the recognition speed, we use a modified branch-and-bound algorithm in the detail-matching module. In the automatic document reading system, characters and punctuation marks are first extracted from printed document images and sorted according to their positions and the document orientation. The system then recognizes all printed Chinese characters between pairs of punctuation marks. The results are then spoken aloud by a speech-synthesis system. The character recognition system and the text-to-speech synthesis system are integrated in the Windows-based document reading system, which provides a user-friendly environment. (C) 1998 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
  • Thumbnail Image
    Item type:Item,
    Speeding-up Chinese character recognition in an automatic document reading system
    (1997)
    Tseng, YH
    ;
    Kuo, CC
    ;
    Lee, HJ
    ;
    交大名義發表
    ;
    資訊工程學系
    In this paper, we present two techniques for speeding up character recognition. Our character recognition system, including the candidate-cluster selection and detail-matching modules, is implemented using two statistical features: crossing-counts and contour-direction counts. In the training stage, we divide characters into different clusters. To keep very high recognition rate, the candidate-cluster selection module selects the top 60 clusters with minimal distances from among 300 predefined clusters. To further speed-up the recognition speed, we use a modified branch-and-bound algorithm in the detail-matching module. In the automatic document-reading system, characters and punctuation marks are first extracted from printed document images and sorted according to their positions and the document orientation. The system then recognizes all printed Chinese characters between pairs of punctuation marks. The results are then spoken aloud by a speech-synthesis system.