Skeleton Representation of Character Based on Multiscale Approach

  • Xinhua You
  • Bin Fang
  • Xinge You
  • Zhenyu He
  • Dan Zhang
  • Yuan Yan Tang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3802)


Character skeleton plays a significant role in character recognition. This paper presents a novel algorithm based on multiscale approach to extract skeletons of printed and hand-written characters. The development of the method is inspired by some desirable characteristics of the modulus minima of wavelet transform. Namely, the local minima of wavelet transform are scale-independent and locate at the medial axis of the symmetrical contours of character stroke. Thus it is particularly suitable for characterizing the inherent skeletons of character strokes. The proposed skeletonization algorithm contains two major steps. First, by thresholding for the modulus minima of wavelet transform, the modulus minima points underlying the character strokes are extracted as the primary skeletons. Based on these primary skeletons, the modulus minima points are being eventually computed as the final skeleton by iteratively performing wavelet transform. The skeleton form the proposed method can be exactly located on the central line of the stroke, and the artifacts and branches of skeletons from traditional methods can be avoided. We tested the algorithm on handwritten and printed character images. Experimental results indicate that the proposed algorithm is applicable to not only binary image but also gray-level image.


Voronoi Diagram Wavelet Function Medial Axis Multiscale Approach Width Structure 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Blum, H.: Biological shape and visual science (part1). J. Theoret, Eds. Biology (1973)Google Scholar
  2. 2.
    Brandt, J.W., Algazi, V.R.: Continuous skeleton computation by voronoi diagram. CVGIP: Image Understanding 55, 329–338 (1992)zbMATHCrossRefGoogle Scholar
  3. 3.
    Chang, H.S., Yan, H.: Analysis of Stroke Structures of Handwritten Chinese Characters. IEEE Trans. Systems, Man., Cybernetics (B) 29, 47–61 (1999)CrossRefGoogle Scholar
  4. 4.
    Ge, Y., Fitzpatrick, J.M.: On the Generation of Skeletons from Discrete Euclidean Distance Maps. IEEE Trans. Pattern Anal. Mach. Intell. 18, 1055–1066 (1996)CrossRefGoogle Scholar
  5. 5.
    Janssen, R.D.T.: Interpretation of maps: from bottom-up to model-based. In: Bunke, H., Wang, P.S.P. (eds.) Handbook of Character Recognition and Document Image Analysis. World Scientific publisher, Singapore (1997)Google Scholar
  6. 6.
    Kgl, B., Krzyżak, A.: Piecewise linear skeletonization using principal curves. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(1), 59–74 (2002)CrossRefGoogle Scholar
  7. 7.
    Lam, L., Lee, S.W., Suen, C.Y.: Thinning Methodologies - a Comprehensive Survey. IEEE Trans. Pattern Anal. Mach. Intell. 14, 869–885 (1992)CrossRefGoogle Scholar
  8. 8.
    Mokhtarian, F., Mackworth, A.K.: A theory of multiscale curvature-based shape representation for planar curves. IEEE Trans. Pattern Anal. Mach. Intell. 14(8), 789–805 (1992)CrossRefGoogle Scholar
  9. 9.
    Ogniewicz, R.L., Kubler, O.: Hierarchic Voronoi skeletons. Pattern Recognition 28, 343–359 (1995)CrossRefGoogle Scholar
  10. 10.
    Rosenfeld, A.: Axial Representations of Shapes. Comput. Vis. Graph. Image Process 33, 156–173 (1986)CrossRefGoogle Scholar
  11. 11.
    Smith, R.W.: Computer Processing of line images: a survey. Pattern Recognition 20, 7–15 (1987)CrossRefGoogle Scholar
  12. 12.
    Tang, Y.Y., You, X.G.: Skeletonization of ribbon-like shapes based on a new wavelet function. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(9), 1118–1133 (2003)CrossRefGoogle Scholar
  13. 13.
    Unser, M., Aldroubi, A., Eden, M.: B-spline signal processing: Part ii-efficient design and applications. IEEE Trans. on SP 41(2), 834–847 (1993)zbMATHCrossRefGoogle Scholar
  14. 14.
    Wang, C., Cannon, D.J., Kumara, R.T., Lu, G.: A Skeleton and Neural Network-Based Approach for Identifying Cosmetic Surface Flaws. IEEE Trans. Neural Networks 6(5), 1201–1211 (1995)CrossRefGoogle Scholar
  15. 15.
    Wang, Y.P., Lee, S.L.: Scale-space derived from B-splines. IEEE Trans. Pattern Anal. Mach. Intell. 20(10), 1040–1050 (1998)CrossRefGoogle Scholar
  16. 16.
    Yang, L.H., You, X.G., Haralick, R.M., Phillips, I.T., Tang, Y.Y.: Characterization of Dirac Edge with New Wavelet Transform. In: Proc. 2th Int. Conf. Wavelets and its Application, vol. 1, pp. 872–878 (2001)Google Scholar
  17. 17.
    Zou, J.J., Yan, H.: Extracting strokes from static line images based on selective searching. Pattern Recognition 32, 935–946 (1999)CrossRefGoogle Scholar
  18. 18.
    Zou, J.J., Yan, H.: Skeletonization of Ribbon-Like shapes Based on Regularity and Singularity Analyses. IEEE Trans. Systems. Man. Cybernetics (B) 31(3), 401–407 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xinhua You
    • 1
  • Bin Fang
    • 3
  • Xinge You
    • 1
    • 2
  • Zhenyu He
    • 2
  • Dan Zhang
    • 1
  • Yuan Yan Tang
    • 1
    • 2
  1. 1.Faculty of Mathematics and Computer ScienceHubei UniversityChina
  2. 2.Department of Computer ScienceHong Kong Baptist University 
  3. 3.Chongqing University 

Personalised recommendations