Advertisement

Knowledge Transfer for Writer Identification

  • Diego Bertolini
  • Luiz S. Oliveira
  • Yandre M. G. Costa
  • Lucas G. Helal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)

Abstract

The technical literature on writer identification usually considers the best case scenario in terms of data availability, i.e., a database composed of hundreds of writers with several documents per writer is available to train the machine learning models. However, in real-life problems such a database may not be available. In this context, learning from one dataset and transferring the knowledge to other would be extremely useful. In this paper we show how to transfer knowledge from one dataset to another through a framework that uses a writer-independent approach based on dissimilarity. Experiments on five different databases under single- and multi-script environments showed that the proposed approach achieves good results. This is an important contribution since it makes it possible do deploy the writer identification system even when no data from that particular writer are available for training.

References

  1. 1.
    Al-Maadeed, S.: Text-dependent writer identification for Arabic handwriting. J. Electr. Comput. Eng. (2012)Google Scholar
  2. 2.
    Bensefia, A., Paquet, T.: Writer identification based on a single handwriting word samples. EURASIP J. Image Video Process. 2016(1), 34 (2016)CrossRefGoogle Scholar
  3. 3.
    Bertolini, D., Oliveira, L.S., Justino, E., Sabourin, R.: Texture-based descriptors for writer identification and verification. Expert Syst. Appl. 40(6), 2069–2080 (2013)CrossRefGoogle Scholar
  4. 4.
    Bertolini, D., Oliveira, L.S., Sabourin, R.: Multi-script writer identification using dissimilarity. In: International Conference on Pattern Recognition (ICPR), pp. 3020–3025. IAPR (2016)Google Scholar
  5. 5.
    Brink, A., Schomaker, L., Bulacu, M.: Towards explainable writer verification and identification using vantage writers. In: 9th International Conference on Document Analysis and Recognition, Curitiba, Brazil, pp. 824–828 (2007)Google Scholar
  6. 6.
    Bulacu, M., Schomaker, L.: Text-independent writer identification and verification using textural and allographic features. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 701–717 (2007)CrossRefGoogle Scholar
  7. 7.
    Chen, J., Kellokumpu, V., Zhao, G., Pietikäinen, M.: RLBP: robust local binary pattern. In: Proceedings of the British Machine Vision Conference (2013)Google Scholar
  8. 8.
    Djeddi, C., Gattal, A., Souici-Meslat, L., Siddiqi, I., Chibani, Y., Abed, H.: LAMIS-MSHD: a multi-script offline handwriting database. In: ICFHR, pp. 93–97 (2014)Google Scholar
  9. 9.
    Djeddi, C., Siddiqi, I., Souici-Meslati, L., Ennaji, A.: Text-independent writer recognition using multi-script handwritten texts. Pattern Recogn. Lett. 34, 1196–1202 (2013)CrossRefGoogle Scholar
  10. 10.
    Freitas, C., Oliveira, L.S., Sabourin, R., Bortolozzi, F.: Brazilian forensic letter database. In: 11th International Workshop on Frontiers on Handwriting Recognition, Montreal, Canada (2008)Google Scholar
  11. 11.
    Hannad, Y., Siddiqi, I., Kettani, M.: Writer identification using texture descriptors of handwritten fragments. Expert Syst. Appl. 47, 14–22 (2016)CrossRefGoogle Scholar
  12. 12.
    Hanusiak, R.K., Oliveira, L.S., Justino, E., Sabourin, R.: Writer verification using texture-based features. Int. J. Doc. Anal. Recogn. (IJDAR) 15(3), 213–226 (2012)CrossRefGoogle Scholar
  13. 13.
    Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man. Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  14. 14.
    Kameya, H., Mori, S., Oka, R.: A segmentation-free biometric writer verification method based on continuous dynamic programming. Pattern Recogn. Lett. 27(6), 567–577 (2006)CrossRefGoogle Scholar
  15. 15.
    Khan, F.A., Tahir, M., Khelifi, F., Bouridane, A., Almotaeryi, R.: Robust off-line text independent writer identification using bagged discrete cosine transform features. Expert Syst. Appl. 71, 404–415 (2017)CrossRefGoogle Scholar
  16. 16.
    Kleber, F., Fiel, S., Diem, M., Sablatnig, R.: CVL-Database: an off-line database for writer retrieval, writer identification and word spotting. In: 12th International Conference on Document Analysis and Recognition, pp. 560–564. IEEE (2013)Google Scholar
  17. 17.
    Maadeed, S.A., Ayouby, W., Hassaïne, A., Aljaam, J.M.: QUWI: an Arabic and English handwriting dataset for offline writer identification. In: 2012 International Conference on Frontiers in Handwriting Recognition, pp. 746–751, September 2012Google Scholar
  18. 18.
    Marti, U.-V., Bunke, H.: The IAM-database: an English sentence database for offline handwriting recognition. Int. J. Doc. Anal. Recogn. 5(1), 39–46 (2002)CrossRefMATHGoogle Scholar
  19. 19.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefMATHGoogle Scholar
  20. 20.
    Ojansivu, V., Heikkilä, J.: Blur insensitive texture classification using local phase quantization. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D. (eds.) ICISP 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-69905-7_27 CrossRefGoogle Scholar
  21. 21.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  22. 22.
    Siddiqi, I., Djeddi, C., Raza, A., Souici-meslati, L.: Automatic analysis of handwriting for gender classification. Pattern Anal. Appl. 18(4), 887–899 (2015)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Siddiqi, I., Vincent, N.: Text independent writer recognition using redundant writing patterns with contour-based orientation and curvature features. Pattern Recogn. 43(11), 3853–3865 (2010)CrossRefMATHGoogle Scholar
  24. 24.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995).  https://doi.org/10.1007/978-1-4757-2440-0 CrossRefMATHGoogle Scholar
  25. 25.
    Wang, X., Ding, X.: An effective writer verification algorithm using negative samples. In: 2004 Ninth International Workshop on Frontiers in Handwriting Recognition, IWFHR-9 2004, pp. 509–513. IEEE (2004)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Federal University of Technology - Paraná (UTFPR)Campo MourãoBrazil
  2. 2.State University of Maringá (UEM)MaringáBrazil
  3. 3.Federal University of Paraná (UFPR)CuritibaBrazil

Personalised recommendations