Advertisement

A Comparative Study of Tree-based Structure Methods for Handwriting Identification

  • Nooraziera Akmal Binti SukorEmail author
  • Azah Kamilah Muda
  • Noor Azilah Muda
  • Choo Yun Huoy
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 285)

Abstract

Handwriting Identification is a process to determine the author of the writing and it involves some of process. Classification process is a final stage of Handwriting Identification process where it will analyze the classification accuracy and based on the number of features selected. In this study, classification process was conducted using various tree-based structure methods. Tree-based structure method is one of the classification methods where it is able to generate a compact subset of non-redundant features and hence improves interpretability and generalization. However its focus is still limited especially in Writer Identification domain. Several of tree-based structure selected and performed using image dataset from IAM Handwriting Database. The results also analyze and compared of each methods of Writer Identification. Random Forest Tree classifier gives the best result with the highest percentage of accuracy followed by J48, Random Tree, REP Tree and Decision Stump.

Keywords

Feature selection Writer identification Tree-based structure Comparative study 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgement

This work is funded by the Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM).

References

  1. 1.
    Xie,J., Wu,J. , Qian,Q. Feature Selection Algorithm Based on Association Rules Mining Method. Eigth IEEE/ACIS International Conference on Computer and Information Science, 2009.Google Scholar
  2. 2.
    Dash, M., & Liu, H. Feature Selection for Classification. Journal of Intelligent Data Analysis, pp.131-156, 1997.Google Scholar
  3. 3.
    Lewis P M. The characteristic selection problem in recognition system. IRE Transaction on Information Theory, 1962, 8, pp.171-178.Google Scholar
  4. 4.
    L. Yu, H. Liu. Efficiently handling feature redundancy in high-dimensional data, in: Proceedings of The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-03), Washington, DC, August, 2003, pp. 685-690.Google Scholar
  5. 5.
    Zexuan Zhu, Yew-Soon Ong, Manoranjan Dash. Wrapper-filter feature selection algorithm using a memetic framework. IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics: a publication of the IEEE Systems, Man, and Cybernetics Society 2007; 37(1): 70-6.Google Scholar
  6. 6.
    R. Kohavi and G. H. John. Wrapper for Feature Subset Selection. Artificial Intelligence, vol. 97, no. 1-2, pp.273-324, 1997.Google Scholar
  7. 7.
    Saeys, Y., Inza, I., &Larranaga, P. A Review of Feature Selection Techniques in Bioinformatics. Journal of Bioinformatics, 2507-2517, 2007.Google Scholar
  8. 8.
    Hall, M. A. (1999). Correlation-based Feature Subset Selection for Machine Learning. Hamilton: University of Waikato.Google Scholar
  9. 9.
    Gadat, S., &Younes, L. A Stochastic Algorithm for Feature Selection in Pattern Recognition. Journal of Machine Learning Research, 509-547, 2007.Google Scholar
  10. 10.
    Portinale, L., &Saitta, L. Feature Selection: State of the Art. In L. Portinale, & L. Saitta, Feature Selection, pp. 1-22, 2002. Alessandria: UniversitadelPiemonte Orientale.Google Scholar
  11. 11.
    S.N. Srihari; Sung-Hyuk Cha and Sangjik Lee, Establishing handwriting individuality using pattern recognition techniques, Document Analysis and Recognition, 2001. Proceedings. Sixth International Conference on, 10-13 Sept. 2001, Pages: 1195 – 1204.Google Scholar
  12. 12.
    S. N. Srihari; Cha, S.-H.; Arora, H.; and Lee, Individuality of Handwriting, Journal of Forensic Sciences, 47(4), , pp. 1-17, July 2002. Google Scholar
  13. 13.
    Bin Zhang and Srihari, S. N., Analysis of Handwriting Individuality Using Word Features, Document Analysis and Recognition. Proceedings. Seventh International Conference Page(s):1142 – 1146, 2003. Google Scholar
  14. 14.
    R. Plamondon and G. Lorette. Automatic signature verification and writer identification – the state of the art. In PatternRecognition, volume 22, pages 107–131, 1989.Google Scholar
  15. 15.
    Schlapbach, A., Bunke, H.: Off-line Handwriting Identification Using HMM Based Recognizers. In: Proc. 17th Int. Conf. on Pattern Recognition, pp. 654–658. IEEE Press, Washington (2004) Google Scholar
  16. 16.
    Bensefia, A., Nosary, A., Paquet, T., Heutte, L.: Writer Identification by Writer’s Invariants. In: Eighth Intl. Workshop on Frontiers in Handwriting Recognition, pp. 274–279. IEEE Press, Washington (2002) Google Scholar
  17. 17.
    B. Zhang, S. Srihari, and S. Lee, Individuality of handwritten characters, in International Conference on Document Analysis and Recognition, (Edinburgh, Scotland), pp. 1086–1090, August 3-6 2003.Google Scholar
  18. 18.
    S. Srihari, S. Cha, H. Arora, and S. Lee, Individuality of handwriting: a validation study, in International Conference on Document Analysis and Recognition, pp. 106–109, 2001.Google Scholar
  19. 19.
    G. Leedham and S.Chachra, Writer identification using innovative binarised features of handwritten numerals, in International Conference on Document Analysis and Recognition, 2003.Google Scholar
  20. 20.
    X. Wang Ding, H. Liu, Writer identification using directional element features and linear transform, in: Proceedings of the 7th International Conference on Document Analysis and Recognition, pp. 942–945, 2003Google Scholar
  21. 21.
    C TomaiI., B. Zhang and S. N. Srihari, Discriminatory power of handwritten words for writer recognition, in International Conference on Pattern Recognition, vol. 2, (Cambridge, UK), pp. 638–641, 2004.Google Scholar
  22. 22.
    E. Zois and V. Anastassopoulos, Morphological waveform coding for writer identification, Pattern Recognition, vol. 33, pp. 385–398, March 2000Google Scholar
  23. 23.
    H. Said, G. Peake, T. Tan, and K. Baker, Writer Identification from Non-Uniformly Skewed Handwriting Images, Proc. Ninth British Machine Vision Conf., pp. 478-487, 1998.Google Scholar
  24. 24.
    A. Bensefia, T. Paquet, L. Heutte, Information retrieval based writer identification, in: Proceedings of the 7th International Conference on Document Analysis and Recognition, 2003, pp. 946–950.Google Scholar
  25. 25.
    S.K.Chan, C. Viard-Gaudin, Y.H. Tay, Online writer identification using character prototypes distributions, in: Proceedings of SPIE—The International Society for Optical Engineering, 2008.Google Scholar
  26. 26.
    G.X. Tan, Automatic writer identification framework for online handwritten document susing character prototypes, Pattern Recognition(2009), Elsevier Ltd.Google Scholar
  27. 27.
    [40] Y. Zhu; Tieniu Tan and Yunhong Wang, Biometric Personal Identification Based on Handwriting, Pattern Recognition, 2000. Proceedings. 15th International Conference on Volume 2, 3-7 Sept 2000 Page(s):797 - 800 vol.2. Recognition, Cambridge, August 23-26, 2004, pp. 654–658 (2004)Google Scholar
  28. 28.
    M. Tapiador, J.A. Sigüenza, Writer identification method based on forensic knowledge, Biometric Authentication: First International Conference, ICBA 2004, Hong Kong, China, July 2004.Google Scholar
  29. 29.
    Chapran, Biometric writer identification: feature analysis and classification, In J. Pattern Recognition Artif. Intell. 20 (4) pp. 483–503, 2006.Google Scholar
  30. 30.
    AzahKamilahMuda&SitiMariyamShamsuddin, A Framework of Artificial Immune System in Writer Identification, Proceeding of International Symposium on Bio-Inspired Computing, 5-7th September, Johor Bahru, 2005Google Scholar
  31. 31.
    AzahKamilahMuda, SitiMariyamHj. Shamsuddin, MaslinaDaru, Embedded Scale United Moment Invariant for Identification of Handwriting Individuality, ICCSA (1) 2007: 385-396Google Scholar
  32. 32.
    F.P. Satrya, Muda A.K, Choo Y.H, MudaN.Computationally Inexpensive Sequential Forward Floating Selection for Acquiring Significant Features for Authorship Invarianceness in Writer Identification, International Journal on New Computer Architectures and Their Applications (IJNCAA) 1(3): 581-598 The Society of Digital Information and Wireless Communications, 2011 (ISSN: 2220-9085).Google Scholar
  33. 33.
    F.P. Satrya, Muda A.K, Choo Y.H, Feature Selection Methods for Writer Identification: A Comparative Study, 2010 International Conference on Computer and Computational Intelligence (ICCCI 2010).Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Nooraziera Akmal Binti Sukor
    • 1
    Email author
  • Azah Kamilah Muda
    • 1
  • Noor Azilah Muda
    • 1
  • Choo Yun Huoy
    • 1
  1. 1.Faculty of Information and CommunicationUniversiti Teknikal Malaysia MelakaMelakaMalaysia

Personalised recommendations