Video OCR: indexing digital new libraries by recognition of superimposed captions

  • Authors:

  • Toshio Sato;Takeo Kanade;Ellen K. Hughes;Michael A. Smith;Shin'ichi Satoh

  • Affiliations:

  • Toshiba Corporation, 70 Yanagi-cho, Saiwai-ku, Kawasaki 210-8501, Japan;School of Computer Science, Carnegie Mellon University, 500 Forbes Avenue, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, 500 Forbes Avenue, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA;National Center for Science Information Systems (NACSIS), 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-8640, Japan

  • Venue:
  • Multimedia Systems - Special section on video libraries
  • Year:
  • 1999

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Abstract

The automatic extraction and recognition of news captions and annotations can be of great help locating topics of interest in digital news video libraries. To achieve this goal, we present a technique, called Video OCR (Optical Character Reader), which detects, extracts, and reads text areas in digital video data. In this paper, we address problems, describe the method by which Video OCR operates, and suggest applications for its use in digital news archives. To solve two problems of character recognition for videos, low-resolution characters and extremely complex backgrounds, we apply an interpolation filter, multiframe integration and character extraction filters. Character segmentation is performed by a recognition-based segmentation method, and intermediate character recognition results are used to improve the segmentation. We also include a method for locating text areas using text-like properties and the use of a language-based postprocessing technique to increase word recognition rates. The overall recognition results are satisfactory for use in news indexing. Performing Video OCR on news video and combining its results with other video understanding techniques will improve the overall understanding of the news video content.