Online Text-Independent Writer Identification Based on Stroke’s Probability Distribution Function

  • Bangyu Li
  • Zhenan Sun
  • Tieniu Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)


This paper introduces a novel method for online writer identification. Traditional methods make use of the distribution of directions in handwritten traces. The novelty of this paper comes from 1)We propose a text-independent writer identification that uses handwriting stroke’s probability distribution function (SPDF) as writer features; 2)We extract four dynamic features to characterize writer individuality; 3)We develop new distance measurement and combine dynamic features in reducing the number of characters required for online text-independent writer identification. In particular, we performed comparative studies of different similarity measures in our experiments. Experiments were conducted on the NLPR handwriting database involving 55 persons. The results show that the new method can improve the identification accuracy and reduce the number of characters required.


text-independent writer identification stroke’s probability distribution function dynamic features 


  1. 1.
    Plamondon, R., Lorette, G.: Automatic signature verification and writer identification -the state of the art. Pattern Recognition, 107–131 (1989)Google Scholar
  2. 2.
    leclerc, F., Plamondon, R.: Automatic signature verification:the state of the art 1989-1993. International Journal of Pattern Recognition and Artificial Intelligence, 643–660 (1994)Google Scholar
  3. 3.
    Tan, T.N., Said, H., Baker, K.D.: Personal identification based on handwriting. Pattern Recognition, 149–160 (2000)Google Scholar
  4. 4.
    Bahlmann, C., Burkhardt, H.: The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping. IEEE Transactions on Pattern Analysis and Machine Intelligence 1, 299–310 (2004)CrossRefGoogle Scholar
  5. 5.
    Schomaker, L., Bulacu, M.: Automatic writer identification using connected-component contours and edge-based features of uppercase western script. IEEE Transactions on Pattern Analysis and Machine Intelligence 6, 787–798 (2004)CrossRefGoogle Scholar
  6. 6.
    Wang, Y., Yu, K., Tan, T.: Writer identification using dynamic features. In: International Conference on Biometrics, pp. 512–518 (July 2004)Google Scholar
  7. 7.
    Messerli, R., Marti, U.V., Bunke, H.: Writer identification using text line based features. In: Internal Conference on Document Anaysis and Recognition, pp. 101–105 (2001)Google Scholar
  8. 8.
    Schomaker, L.R.B., Plamondon, R.: The relation between pen force and pen-point kinematics in handwriting. Biological Cybernetics, 277–289 (1990)Google Scholar
  9. 9.
    Methasate, I., Sae-Tang, S.: On-line thai handwriting character recognition using stroke segmentation with hmm. In: International conference on Applied informatis-Artificial Intelligence and Applications, pp. 59–62 (2002)Google Scholar
  10. 10.
    Kim, I.-J., Kim, J.-H.: Statistical character structure modeling and its application to handwritten chinese character recognition. Biological Cybernetics, 1422–1436 (2003)Google Scholar
  11. 11.
    Wang, Y., Zhu, Y., Tan, T.: Biometric personal identification based on handwriting. International Conference on Pattern Recognition, 801–804 (2001)Google Scholar
  12. 12.
    Tan, T.N.: Texture feature extraction via cortical channel modeling. In: Proc.11th IAPR Inter. Conf. Pattern Recognition, pp. 607–610 (1992)Google Scholar
  13. 13.
    Leedham, G., Chachra, S.: Writer identification using innovative binarised features of handwritten numerals. In: Internal Conference on Document Anaysis and Recognition, pp. 413–417 (2003)Google Scholar
  14. 14.
    Mertzios, B.G., Tsirikolias, K.: Statistical pattern recognition using efficient two-dimensional moments with applications to character recognition. Pattern recognition, 877–882 (1993)Google Scholar
  15. 15.
    maarse, F., Thomassen, A.: Produced and perceived writing slant:differences between up and down strokes. Acta psychologica 3, 131–147 (1983)CrossRefGoogle Scholar
  16. 16.
    Schomaker, L., maarse, F., Teulings, H.-L.: Automatic identification of writers.human-computer interaction: Psychonomic aspects, pp. 353–360. Springer, Heidelberg (1988)Google Scholar
  17. 17.
    Crettez, J.-P.: A set of handwriting families:style recognition. In: Internal Conference on Document Anaysis and Recognition, pp. 489–494 (1995)Google Scholar
  18. 18.
    Serratosa, F., Sanfeliu, A.: Signatures versus histograms: Defintions, distances and algorithms. Pattern Recgnition, 921–934 (2006)Google Scholar
  19. 19.
    Jain, A.K., Nandakumar, K., Ross, A.: Score normalization in multimodal biometric systems. Pattern Recognition 12, 2270–2285 (2005)CrossRefGoogle Scholar
  20. 20.
    Yeung, D.Y., Chang, H., Xiong, Y., George, S., Kashi, R., Matsumoto, T., Rigoll, G.: Svc 2004: First international signature verification competition. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 16–22. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Bangyu Li
    • 1
  • Zhenan Sun
    • 1
  • Tieniu Tan
    • 1
  1. 1.Center for Biometrics and Security Research, National Lab of Pattern Recognition, Institute of Automation, Chinese Academy of Science, BeijingP.R. China

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