A Binarization Method for a Scenery Image with the Fractal Dimension

  • Hiromi Yoshida
  • Naoki Tanaka
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


We propose a new binarization method suited for character extraction from a sign board in a scenery image. The binarization is thought to be a significant step in character extraction in order to get high quality result. Character region of sigh board, however, has many variation and colors. In addition to it, if there exists high frequency texture region like a mountain or trees in the background, it can be a cause of difficulty to binarize an image. At the high frequency region, the binarized result is sensitive to the threshold change. On the other hand, a character region of sign board consists of solid area, that is, includes few high frequency regions, and has relatively high contrast. So the binarized result of character region is stabile against an interval of the threshold value. Focusing attention on this point, we propose a new method which obtains a threshold value based on the fractal dimension to evaluate both region’s density and stability to threshold change. Through the proposed method, we can get a fine quality binarized images, where the characters can be extracted correctly.


Binarization Fractal dimension Blanket method 


  1. 1.
    Trier, O.D., Jain, A.K.: Goal-Directed Evaluation of Binarization Methods. IEEE Trans. Pattern Analysis and Machine Inteligence 7(12), 1191–1201 (1995)CrossRefGoogle Scholar
  2. 2.
    Niblack, W.: An Introduction to Digital Image Processing, pp. 115–116. Prentice Hall, Englewood Cliffs (1986)Google Scholar
  3. 3.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans, Systems, Man, and Cybernetics 9(1), 62–66 (1979)CrossRefMathSciNetGoogle Scholar
  4. 4.
    Yoshida, H., Tanaka, N.: A Binarization method for Crack Detection in a Road Surface Image with the Fractal Dimension. In: Proceedings of MVA 2009 IAPR Conference on Machine Vision Applications, pp. 70–73 (2009)Google Scholar
  5. 5.
    Peleg, S., Naor, J., Hartley, R., Avnir, D.: Multiple resolution texture analysis and classification. IEEE Trans. Pattern Analysis and Machine Inteligence 6(4), 518–523 (1984)CrossRefGoogle Scholar
  6. 6.
    Noviant, S., Suzuki, Y., Maeda, J.: Optimumestimation of local fractal dimension based on the blanket method. IPSJ Journal 43(3), 825–828 (2002)MathSciNetGoogle Scholar
  7. 7.
    Wang, Y., Tanaka, N.: Text String Extraction from Scene Image Based on Edge Feature and Morphology. In: Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, pp. 323–328 (2008)Google Scholar
  8. 8.
    Yoshida, H., Tanaka, N.: A Study on Signboard Image Identification with SIFT Features. In: Handout of 8th IAPR International Workshop on Document Analysis Systems, pp. 10–14 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Hiromi Yoshida
    • 1
  • Naoki Tanaka
    • 1
  1. 1.Graduate School of Maritime ScienceKobe University 

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