Advertisement

Study of Zone-Based Feature for Online Handwritten Signature Recognition and Verification in Devanagari Script

  • Rajib GhoshEmail author
  • Partha Pratim Roy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 460)

Abstract

This paper presents one zone-based feature extraction approach for online handwritten signature recognition and verification of one of the major Indic scripts–Devanagari. To the best of our knowledge no work is available for signature recognition and verification in Indic scripts. Here, the entire online image is divided into a number of local zones. In this approach, named Zone wise Slopes of Dominant Points (ZSDP), the dominant points are detected first from each stroke and next the slope angles between consecutive dominant points are calculated and features are extracted in these local zones. Next, these features are supplied to two different classifiers; Hidden Markov Model (HMM) and Support Vector Machine (SVM) for recognition and verification of signatures. An exhaustive experiment in a large dataset is performed using this zone-based feature on original and forged signatures in Devanagari script and encouraging results are found.

Keywords

Online handwriting Signature recognition Signature verification Zone-wise feature Dominant points SVM and HMM 

References

  1. 1.
    W. Nelson, E. Kishon, “Use of Dynamic Features for Signature Verification”, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1991, Charlottesville, USA, pp. 201–205.Google Scholar
  2. 2.
    R. Ghosh, P.P. Roy, “Study of two zone based features for online Bengali and Devanagari character recognition”, Proceedings of the 13th International Conference on Document Analysis and Recognition (ICDAR), 2015, Nancy, France, pp. 401–405.Google Scholar
  3. 3.
    R. Plamondon, G. Lorette, “Automatic signature verification and writer identification- the state of the art”, Pattern Recognition, 1989, Vol. 22, Issue 2, pp. 107–131.Google Scholar
  4. 4.
    M. Parezeau, R. Plamendon, “A Comparative Analysis of Regional Correlation, Dynamic Time Warping and Skeleton Matching for Signature Verification”, IEEE Transaction on Pattern Recognition and Machine Intelligence, 1990, Vol. 12, Issue 7, pp. 710–717.Google Scholar
  5. 5.
    Y. Sato, K. Kogure, “Online signature verification based on shape, motion and writing”, Proceedings of the 6th International Conference on Pattern Recognition, 1982, Munich, Germany, pp. 823–826.Google Scholar
  6. 6.
    P. Zhao, A. Higashi, Y. Sato, “On-line signature verification by adaptively weighted DP matching”, IEICE Transaction on Information System, 1996, Vol. E79-D, Issue 5, pp. 535–541.Google Scholar
  7. 7.
    W.T. Nelson, W. Turin, T. Hastie, “Statistical Methods for On-Line Signature Verification”, International Journal of Pattern Recognition and Artificial Intelligence, 1994, Vol. 8, Issue 3, pp. 749–770.Google Scholar
  8. 8.
    L.L. Lee, T. Berger, E. Aviczer, “Reliable On-Line Signature Verification Systems”, IEEE Transaction on Pattern Analysis and Machine Intelligence, 1996, Vol. 18, Issue 6, pp. 643–649.Google Scholar
  9. 9.
    D. Letjman, S. Geoge, “On-Line Handwritten Signature Verification Using Wavelet and Back Propagation Neural Networks”, Proceedings of the 6th International Conference on Document Analysis and Recognition (ICDAR), 2001, Seattle, USA, pp. 596–598.Google Scholar
  10. 10.
    Q.Z. Wu, S.Y. Lee, I.C. Jou, “On-Line Signature Verification Based on Logarithmic Spectrum”, Pattern Recognition, 1998, Vol. 31, Issue 12, pp. 1865–1871.Google Scholar
  11. 11.
    G. Dimauro, G. Impedevo, G. Pirlo, “Component Oriented Algorithms for Signature Verification”, International Journal of Pattern Recognition and Artificial Intelligence, 1994, Vol. 8, Issue 3, pp. 771–794.Google Scholar
  12. 12.
    J.F. Aguilar, S. Krawczyk, J.O. Garcia, A.K. Jain, “Fusion of Local and Regional Approaches for On-Line Signature Verification”, Proceedings of the International Workshop on Biometric Recognition System, 2005, Beijing, China, pp. 188–196.Google Scholar
  13. 13.
    S. Jaeger, S. Manke, J. Reichert, A. Waibel, “Online handwriting recognition: The NPen++ recognizer,” International Journal on Document Analysis and Recognition, 2001, Volume 3, Issue 3, pp. 169–180.Google Scholar
  14. 14.
    C. Burges, “A tutorial on support vector machines for pattern recognition”, Data Mining and Knowledge Discovery, vol.2, pp. 1–43.Google Scholar
  15. 15.
    U. Pal, P. P. Roy, N. Tripathy, J. Lladós, “Multi-Oriented Bangla and Devanagari Text Recognition”, Pattern Recognition, vol. 43, 2010, pp. 4124–4136.Google Scholar
  16. 16.
    P.P. Roy, P. Dey, S. Roy, U. Pal, F. Kimura, “A Novel Approach of Bangla Handwritten Text Recognition using HMM”, Proceedings of the 14th International Conference on Frontiers in Handwriting Recognition, 2014, Heraklion, Greece, pp. 661–666.Google Scholar
  17. 17.
    S. Young. The HTK Book, Version 3.4. Cambridge Univ. Eng. Dept., 2006.Google Scholar

Copyright information

© Springer Science+Business Media Singapore 2017

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringNational Institute of TechnologyPatnaIndia
  2. 2.Department of Computer Science & EngineeringIndian Institute of TechnologyRoorkeeIndia

Personalised recommendations