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Author: search.filters.author.Kuroki, YoshimitsuSubject: search.filters.subject.natural scene

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    Text-background decomposition for thai text localization and recognition in natural scenes
    (2014-01-12)
    Woraratpanya, Kuntpong
    ;
    Pasupa, Kitsuchart
    ;
    Suttapakti, Ungsumalee
    ;
    Boonchukusol, Pimlak
    ;
    Titijaroonroj, Taravichet
    Thai text localization and recognition in natural scenes is still a grand challenge in current applications. However, the efficiency of recognition rates depends on text localization, i.e., the higher purity of text-background decomposition leads to the higher accuracy rate of character recognition. In order to achieve this purpose, the text-background decomposition methods, namely adaptive boundary clustering (ABC) and n-point boundary clustering (n-PBC), are proposed to improve a precision of text localization. These methods are evaluated by self-en-tropy for purity measure. Based on 300 test images, the experimental results demonstrate that the ABC method achieves the very low self-entropy, i.e., the low self-entropy implies the good decomposition of text and background. Furthermore, based on 8,077 characters in natural scene test images, the ABC method helps increase the precision of text localization and improves the accuracy rate of character recognition, when compared to the conventional methods.
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    Improved Thai text detection from natural scenes
    (2013-01-01)
    Woraratpanya, Kuntpong
    ;
    Boonchukusol, Pimlak
    ;
    Kuroki, Yoshimitsu
    ;
    Kato, Yasushi
    Thai text detection from natural scenes is still a challenging task for language translation applications, since there are many unsolved issues. Furthermore, the existing related works cannot completely detect Thai text. The main reason is that Thai text layout has vowels and tonal marks that differ from other languages. This paper proposes an approach to detect Thai text from natural scenes. The approach consists of two main procedures. (i) Fast boundary clustering algorithm decomposes scene features into multilayers, so that it is faster and easier to analyze Thai text characters. (ii) Modified connected component analysis method is applied to such scene features in order to detect Thai text boundaries. Based on 150 test images with 4,920 characters, the experimental results demonstrate that the proposed approach achieves the high average precision and recall, 0.80 and 0.90. © 2013 IEEE.