Fast and robust text detection in images and video frames

  • Authors:

  • Qixiang Ye;Qingming Huang;Wen Gao;Debin Zhao

  • Affiliations:

  • Institute of Computing Technology, Chinese Academy of Sciences, China;Graduate School, Chinese Academy of Sciences, China;Institute of Computing Technology, Chinese Academy of Sciences, China and Graduate School, Chinese Academy of Sciences, China;Department of Computer Science, Harbin Institute of Technology, China

  • Venue:
  • Image and Vision Computing
  • Year:
  • 2005

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Abstract

Text in images and video frames carries important information for visual content understanding and retrieval. In this paper, by using multiscale wavelet features, we propose a novel coarse-to-fine algorithm that is able to locate text lines even under complex background. First, in the coarse detection, after the wavelet energy feature is calculated to locate all possible text pixels, a density-based region growing method is developed to connect these pixels into regions which are further separated into candidate text lines by structural information. Secondly, in the fine detection, with four kinds of texture features extracted to represent the texture pattern of a text line, a forward search algorithm is applied to select the most effective features. Finally, an SVM classifier is used to identify true text from the candidates based on the selected features. Experimental results show that this approach can fast and robustly detect text lines under various conditions.