Precise news video text detection/localization based on multiple frames integration

  • Authors:

  • Shwu-Huey Yen;Hsiao-Wei Chang

  • Affiliations:

  • Department of Computer Science and Information Engineering, Tamkang University, Tamsui, Taipei County, Taiwan, Republic of China;Department of Computer Science and Inf. Eng., Tamkang Univ., Tamsui, Taipei County, Taiwan, Republic of China and Department of Computer Science and Information Eng., China Univ. of Science and Te ...

  • Venue:
  • ISCGAV'10 Proceedings of the 10th WSEAS international conference on Signal processing, computational geometry and artificial vision
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper presents a multiple frames integration based approach to detect and localize static caption texts on news videos. Utilizing the temporal information of videos, the algorithm includes robust text features and the non-text line deletion technique, and yields precise and tight localization for detected text regions. The Canny edge detector is first applied on reference frames and is followed by executing the logical AND to reduce the edges from the variation of the background including the scrolling texts. Next, rough text candidate regions are determined by calculating the number black-white transition (BWT). Finally, the text regions are refined by the non-text line deletion technique. The proposed algorithm is applicable to multiple languages and robust to text polarities, alignments, and character sizes (from 10×10 to 30×30). According to the experimental results on various multilingual video sequences, the proposed algorithm has a 96% and above performance in recall, precision, and quality of bounding preciseness.