International Journal of Contents

  • 제9권2호
  • /
  • Pages.8-13
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  • 2013
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  • 1738-6764(pISSN)
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  • 2093-7504(eISSN)
한국콘텐츠학회 (The Korea Contents Association)
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Systematic Approach for Detecting Text in Images Using Supervised Learning

  • Nguyen, Minh Hieu (Department of Electronics and Computer Engineering, Chonnam National University) ;
  • Lee, GueeSang (Department of Electronics and Computer Engineering, Chonnam National University)
  • 투고 : 2013.02.27
  • 심사 : 2013.06.10
  • 발행 : 2013.06.28
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초록

Locating text data in images automatically has been a challenging task. In this approach, we build a three stage system for text detection purpose. This system utilizes tensor voting and Completed Local Binary Pattern (CLBP) to classify text and non-text regions. While tensor voting generates the text line information, which is very useful for localizing candidate text regions, the Nearest Neighbor classifier trained on discriminative features obtained by the CLBP-based operator is used to refine the results. The whole algorithm is implemented in MATLAB and applied to all images of ICDAR 2011 Robust Reading Competition data set. Experiments show the promising performance of this method.

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참고문헌

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