Text Extraction in Digital News Video Using Morphology

  • Hyeran Byun
  • Inyoung Jang
  • Yeongwoo Choi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2423)

Abstract

In this paper, a new method is presented to extract both superimposed and embedded scene texts in digital news videos. The algorithm is summarized in the following three steps : preprocessing, extracting candidate regions, and filtering candidate regions. For the first preprocessing step, a color image is converted into a gray-level image and a modified local adaptive thresholding is applied to the contrast-stretched image. In the second step, various morphological operations and Geo-correction method are applied to remove non-text components while retaining the text components. In the third filtering step, non-text components are removed based on the characteristics of each candidate component such as the number of pixels and the bounding box of each connected component Acceptable results have been obtained using the proposed method on 300 domestic news images with a recognition rate of 93.6%. Also, the proposed method gives good performance on the various kinds of images such as foreign news and film videos.

Keywords

Text Component Text Line Morphological Operation Text Region Scene Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Jae-Chang Shim, Chitra Dorai, and Ruud Bolle, Automatic Text Extraction from Video for Content-Based Annotation and Retrieval, Proceedings of Fourteenth International Conference on Pattern Recognition, Vol. 1, pp. 618–620, 1998.Google Scholar
  2. 2.
    Anil K. Jain and Bin Yu, Automatic text location in images and video frames, Pattern Recognition, Vol. 31, No. 12, pp. 2055–2076, 1998.CrossRefGoogle Scholar
  3. 3.
    H. Kuwano, Y. Taniguchi, H. Arai, M. Mori, S. Kuraka-ke, and H. Kojima, Telop-on-demand: video structuring and retrieval based on text recognition, IEEE International Conference on Multimedia and Expo, Vol. 2, pp. 759–762, 2000.Google Scholar
  4. 4.
    U. Gargi, S. Antani, and R. Kasturi, Indexing text events in digital video databases, Proceedings of Fourteenth International Conference on Pattern Recognition, Vol. 1, pp. 916–918, 1998.Google Scholar
  5. 5.
    Sameer Antani, Ullas Gargi, David Crandall, Tarak Gandhi, and Rangachar Kasturi, Extraction of Text in Video, Dept. of Computer. Science and Eng., Pennsylvania State Univ., Technical Report, CSE-99-016, 1999.Google Scholar
  6. 6.
    S. Messelodi and C.M. Modena, Automatic identification and skew estimation of text lines in real scene images, Pattern Recognition, Vol. 32, pp. 791–810, 1999.CrossRefGoogle Scholar
  7. 7.
    S. Antani, D. Crandall, and R. Kasturi, Robust extraction of text in video, Proceedings of 15th International Conference on Pattern Recognition, Vol. 1, pp. 831–834, 2000.Google Scholar
  8. 8.
    Y. Lu, Machine printed character segmentation-An overview, Pattern Recognition, Vol. 28, pp. 67–80, 1995.CrossRefGoogle Scholar
  9. 9.
    Y. Zhong, K. Karu, and A. K. Jain, Locating text in complex color images, Pattern Recognition, Vol. 28, pp. 1523–1535, 1995.CrossRefGoogle Scholar
  10. 10.
    J. Ohya, A. Shio, and S. Akamatsu, Recognizing characters in scene images, IEEE Trans on Pattern Analysis and Machine Intelligence. PAMI-16, pp.214–220, 1994.Google Scholar
  11. 11.
    H. K. Kim, Efficient automatic text location method and content-based indexing and structuring of video database, J. Visual Commun. Image Representation, Vol. 7, pp. 336–344, 1996.CrossRefGoogle Scholar
  12. 12.
    M. A. Smith and T. Kanade, Video skimming for quick browsing base on audio and image characterization, Technical Report CMU-CS-95-186, Carnegie Mellon University, July 1995.Google Scholar
  13. 13.
    J. Serra, Image Analysis and Mathematical Morphology. New York: Academic, 1982.MATHGoogle Scholar
  14. 14.
    Pyeoung-Kee Kim, Automatic Text Location in Complex Color Images using Local Color Quantization, TENCON 99. Proceedings of the IEEE Region 10 Conference, Vol. 1, pp. 629–632, 1999.Google Scholar
  15. 15.
    R. Lienhart and F. Stuber, Automatic text recognition in digital videos, SPIE Image and Video Processing IV, pp 2666–2669, 1996Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Hyeran Byun
    • 1
  • Inyoung Jang
    • 1
  • Yeongwoo Choi
    • 2
  1. 1.Visual Information Processing Lab., Dept. of Computer ScienceYonsei UniversitySeoulKorea
  2. 2.Image Processing Lab., Dept. of Computer ScienceSookmyung Women’s UniversitySeoulKorea

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