A framework for improved video text detection and recognition

  • Authors:

  • Haojin Yang;Bernhard Quehl;Harald Sack

  • Affiliations:

  • Hasso-Plattner-Institute for IT-Systems Engineering, University of Potsdam, Potsdam, Germany 14467;Hasso-Plattner-Institute for IT-Systems Engineering, University of Potsdam, Potsdam, Germany 14467;Hasso-Plattner-Institute for IT-Systems Engineering, University of Potsdam, Potsdam, Germany 14467

  • Venue:
  • Multimedia Tools and Applications
  • Year:
  • 2014

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Abstract

Text displayed in a video is an essential part for the high-level semantic information of the video content. Therefore, video text can be used as a valuable source for automated video indexing in digital video libraries. In this paper, we propose a workflow for video text detection and recognition. In the text detection stage, we have developed a fast localization-verification scheme, in which an edge-based multi-scale text detector first identifies potential text candidates with high recall rate. Then, detected candidate text lines are refined by using an image entropy-based filter. Finally, Stroke Width Transform (SWT)- and Support Vector Machine (SVM)-based verification procedures are applied to eliminate the false alarms. For text recognition, we have developed a novel skeleton-based binarization method in order to separate text from complex backgrounds to make it processible for standard OCR (Optical Character Recognition) software. Operability and accuracy of proposed text detection and binarization methods have been evaluated by using publicly available test data sets.