Statistical Analysis in Signature Recognition System Based on Levenshtein Distance

  • Malgorzata PalysEmail author
  • Rafal Doroz
  • Piotr Porwik
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 226)


In this paper we develop our previously presented studies, where adaptation of the Levenshtein method in a signature recognition process is proposed. Three methods based on the normalized Levenshtein measure were taken into consideration. The studies included an analysis and selection of appropriate signature features, on the basis of which the authenticity of a signature was verified later. A statistical apparatus was used to perform a comprehensive analysis. Results obrained were tested by means of χ 2 independence test. It allowed determining the relationship between signature features and the errors of classifier.


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  1. 1.
    Cha, S.: Comprehensive survey on distance/similarity measures between probability density functions. International Journal of Mathematical Models and Methods in Applied Sciences 1(4), 300–307 (2007)MathSciNetGoogle Scholar
  2. 2.
    Doroz, R., Porwik, P.: Handwritten signature recognition with adaptive selection of behavioral features. In: Chaki, N., Cortesi, A. (eds.) CISIM 2011. CCIS, vol. 245, pp. 128–136. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  3. 3.
    Doroz, R., Wróbel, K.: Method of signature recognition with the use of the mean differences. In: Proceedings of the ITI 2009 31st International Conference (ITI 2009), pp. 231–235 (2009)Google Scholar
  4. 4.
    Froelich, W., Wakulicz-Deja, A.: Probabilistic Similarity-Based Reduct. In: Yao, J., Ramanna, S., Wang, G., Suraj, Z. (eds.) RSKT 2011. LNCS, vol. 6954, pp. 610–615. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  5. 5.
    Gupta, G.K.: The state of the art in on-line handwritten signature verification4 (2006)Google Scholar
  6. 6.
    Impedovo, S., Pirlo, G.: Verification of handwritten signatures: an overview. In: 14th International Conference on Image Analysis and Processing (ICIAP 2007), pp. 191–196 (2007)Google Scholar
  7. 7.
    Khan, M.K., Khan, M.A., Khan, M.A.U., Ahmad, I.: On-line signature verification by exploiting inter-feature dependencies. In: 18th International Conference on Pattern Recognition (ICPR 2006), vol. 2, pp. 796–799 (2006)Google Scholar
  8. 8.
    Koprowski, R., Wrobel: The cell structures segmentation. In: 4th International Conference on Computer Recognition Systems (CORES 2005), pp. 569–576 (2005)Google Scholar
  9. 9.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, Insertions, And Reversals. In: Soviet Physics Dokl, pp. 707–710 (1966)Google Scholar
  10. 10.
    Marzal, A., Vidal, E.: Computation of normalized edit distance and applications. IEEE Trans. Pattern Analysis and Machine Intelligence 15(9), 926–932 (1993)CrossRefGoogle Scholar
  11. 11.
    Para, T., Mitas, M.: Determining signatures characteristic features using statistical methods. Journal of Medical Informatics and Technologies 1, 41–50 (2008)Google Scholar
  12. 12.
    Pastor, M., Toselli, A., Vidal, E.: Writing speed normalization for on-line handwritten text recognition. In: Proceedings of the 2005 Eight International Conference on Document Analysis and Recognition, pp. 1131–1135 (2005)Google Scholar
  13. 13.
    Schimke, S., Vielhauer, C., Dittmann, J.: Using adapted Levenshtein distance for on-line signature authentication. In: Proceedings of the 17th International Conference, vol. 2, pp. 931–934 (2004)Google Scholar
  14. 14.
    Weigel, A., Fein, F.: Normalizing the weighted edit distance. In: Proc. 12th IAPR Intl Conf. Pattern Recognition, Conf. B: Computer Vision and Image Processing., vol. 2, pp. 399–402 (1994)Google Scholar

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© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

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