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Detecting Text in Natural Scenes Based on a Reduction of Photometric Effects: Problem of Text Detection

  • Alain Trémeau
  • Basura Fernando
  • Sezer Karaoglu
  • Damien Muselet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6626)

Abstract

In this paper, we propose a novel method for detecting and segmenting text layers in complex images. This method is robust against degradations such as shadows, non-uniform illumination, low-contrast, large signaldependent noise, smear and strain. The proposed method first uses a geodesic transform based on a morphological reconstruction technique to remove dark/light structures connected to the borders of the image and to emphasize on objects in center of the image. Next uses a method based on difference of gamma functions approximated by the Generalized Extreme Value Distribution (GEVD) to find a correct threshold for binarization. The main function of this GEVD is to find the optimum threshold value for image binarization relatively to a significance level. The significance levels are defined in function of the background complexity. In this paper, we show that this method is much simpler than other methods for text binarization and produces better text extraction results on degraded documents and natural scene images.

Keywords

Text binarization Contrast enhancement Gamma function Photometric invariants Color invariants 

References

  1. 1.
    van de Weijer, J., Gevers, T., Geusebroek, J.M.: Edge and corner detection by photometric quasi-invariants. IEEE Trans. on Pattern Analysis and Machine Intelligence 27(4), 625–630 (2005)CrossRefGoogle Scholar
  2. 2.
    Li, B., Xue, X., Fan, J.: A robust incremental learning framework for accurate skin region segmentation in color images. Pattern Recognition 40(12), 3621–3632 (2007)CrossRefzbMATHGoogle Scholar
  3. 3.
    Moreno-Noguer, F., Sanfeliu, A., Samaras, D.: Integration of deformable contours and a multiple hypotheses Fischer color model for robust tracking in varying illuminant environments. Image and Vision Computing 25, 285–296 (2007)CrossRefGoogle Scholar
  4. 4.
    Trémeau, A., Tominaga, S., Plataniotis, K.: Color in Image and Video Processing: most recent trends and future research directions. EURASIP Journal on Image and Video Processing 2008, article ID 581371, 26 p. (2008)Google Scholar
  5. 5.
    Gevers, T., van de Weijer, J., Stokman, H.: Color feature detection. In: Color Image Processing: Methods and Applications Book, ch. 9, pp. 203–226. CRC press, Boca Raton (2007)Google Scholar
  6. 6.
    Koschan, A., Abidi, M.: Detection and classification of edges in color images. IEEE Signal Processing Magazine, 64–73 (January 2005)Google Scholar
  7. 7.
    Salvador, E., Cavallaro, A., Ebrahimi, T.: Cast shadow segmentation using invariant color features. Computer Vision and Image Understanding 95, 238–259 (2004)CrossRefGoogle Scholar
  8. 8.
    Dong, G., Xie, M.: Color clustering and learning for image segmentation based on neural networks. IEEE Trans. on Neural Networks 16, 925–936 (2005)CrossRefGoogle Scholar
  9. 9.
    Trémeau, A., Godau, C., Karaoglu, S., Muselet, D.: Detecting text in natural scenes based on a reduction of photometric effects: problem of color invariance. In: Schettini, R., Tominaga, S., Trémeau, A. (eds.) CCIW 2011. LNCS, vol. 6626, pp. 234–248. Springer, Heidelberg (2011)Google Scholar
  10. 10.
    Karatzas, D., Antonacopoulos, A.: Colour text segmentation in web images based on human perception. Image and Vision Computing 25, 564–577 (2007)CrossRefzbMATHGoogle Scholar
  11. 11.
    Fernando, B., Karaoglu, S., Trémeau, A.: Extreme value theory based text binarization in documents and natural scenes. In: Proceedings of IEEE, ICMV, Hong-Kong (to be published)Google Scholar
  12. 12.
    Karaoglu, S., Fernando, B., Trémeau, A.: A Novel Algorithm for Text Detection and Localization in Natural Scene Images. In: Proceedings of IEEE, DICTA 2010, Sydney, Australia, December 1-3 (2010) (to be published) Google Scholar
  13. 13.
    ICDAR 2003 robust reading competitions. In: Proc. of 7th Intl. Conf. on Document Analysis and Recognition, pp. 682–687 (2003)Google Scholar
  14. 14.
    ICDAR 2003 text locating competition results. In: Proc. of 8th Intl. Conf. on Document Analysis and Recognition, pp. 80–84(1) (2005)Google Scholar
  15. 15.
    Document Image Binarization Contest (DIBCO 2009) in the framework of ICDAR2009. In: Proc. of 10th Intl. Conf. on Document Analysis and Recognition, pp. 1375–1382 (2009)Google Scholar
  16. 16.
    Lienhart, R., Wernickle, A.: Localizing and segmenting text in images and videos. IEEE Trans. on Circuits and Systems for Video Technology 12(4), 256–268 (2002)CrossRefGoogle Scholar
  17. 17.
    Niblack, W.: An Introduction to Image Processing, pp. 115–116. Prentice-Hall, Englewood Cliffs (1986)Google Scholar
  18. 18.
    Sauvola, J., Pietaksinen, M.: Adaptive document image binarization. Pattern Recogn. 33, 225–236 (2000)CrossRefGoogle Scholar
  19. 19.
    Trier, O.D., Taxt, T.: Evaluation of binarization methods for document images. IEEE Trans. Pattern Anal. Machine Intell. 17, 312–315 (1995)CrossRefGoogle Scholar
  20. 20.
    Lim, J., Park, J., Medioni, G.G.: Text segmentation in color images using tensor voting. Image and Vision Computing 25, 671–685 (2007)CrossRefGoogle Scholar
  21. 21.
    Soille, P.: Morphological Image Analysis: Principles and Applications, pp. 182–198. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  22. 22.
    Coles, S.: An Introduction to Statistical Modeling of Extreme Values, pp. 45–50, 75-78. Springer, Heidelberg (2001) ISBN 1-85233-459-2, zbMATHGoogle Scholar
  23. 23.
    Lawless, J.F.: Statistical Models and Methods for Lifetime Data, pp. 211–255. Wiley, New York (1982)zbMATHGoogle Scholar
  24. 24.
    Prescott, P.: Parameter estimation for the generalized extreme value distribution. Journal of Statistical Computation and Simulation 16(3&4), 241–250 (1983)CrossRefzbMATHGoogle Scholar
  25. 25.
    Pickands, J.: Statistical inference using extreme order statistics. The Annals of Statistics 3, 119–131 (1975)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Behrens, C.N., Lopes, H.F., Gamerman, D.: Bayesian Analysis of Extreme Events with Threshold Estimation. Statistical Modeling 4(3), 227–244 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  27. 27.
    Otsu, N.: A threshold selection method from graylevel histograms. IEEE Trans. Systems Man Cybernet. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  28. 28.
    Álvarez, J.M., Gevers, T., López, A.M.: Learning Photometric Invariance for Object Detection. Int. J. Comput. Vis. 90, 45–61 (2010)CrossRefGoogle Scholar
  29. 29.
    Pratikakis, I., Gatos, B., Ntirogiannis, K.: H-DIBCO 2010 - Handwritten Document Image Binarization Competition. In: Proceedings of the 12th International Conference on Frontiers in Handwriting Recognition, pp. 727–732 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alain Trémeau
    • 1
  • Basura Fernando
    • 2
  • Sezer Karaoglu
    • 2
  • Damien Muselet
    • 1
  1. 1.Laboratoire Hubert CurienUniversity Jean MonnetSaint EtienneFrance
  2. 2.Erasmus Mundus CIMET MasterUniversity Jean MonnetSaint EtienneFrance

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