Robust news video text detection based on edges and line-deletion

  • Authors:

  • Shwu-Huey Yen;Hsiao-Wei Chang;Chia-Jen Wang;Chun-Wei Wang

  • Affiliations:

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

  • Venue:
  • WSEAS Transactions on Signal Processing
  • Year:
  • 2010

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Abstract

This paper presents a robust and efficient text detection algorithm for news video. The proposed algorithm uses the temporal information of video and logical AND operation to remove most of irrelevant background. Then a window-based method by counting the black-and-white transitions is applied on the resulted edge map to obtain rough text blobs. Line deletion technique is used twice to refine the text blocks. The proposed algorithm is applicable to multiple languages (English, Japanese and Chinese), robust to text polarities (positive or negative), various character sizes (from 4×7 to 30×30), and text alignments (horizontal or vertical). Three metrics, recall (R), precision (P), and quality of bounding preciseness (Q), are adopted to measure the efficacy of text detection algorithms. According to the experimental results on various multilingual video sequences, the proposed algorithm has a 96% and above performance in all three metrics. Comparing to existing methods, our method has better performance especially in the quality of bounding preciseness that is crucial to later binarization process.