HEURISTIC TECHNIQUES FOR HANDWRITTEN SIGNATURE CLASSIFICATION

Authors

  • Marcin Adamski
  • Khalid Saeed

DOI:

https://doi.org/10.47839/ijc.5.2.401

Keywords:

Signature classification, offline recognition, dynamic time warping

Abstract

New theoretical and experimental techniques for offline classification of handwritten signatures are introduced in this paper. The proposed algorithms are mainly based on boundary tracing technique for extracting characteristic features. Outer and inner boundaries of the signature image are treated separately. The upper and lower parts of the boundaries are extracted to form two sequences of points. Three algorithms for calculating feature vectors are applied based on y coordinate, distances between consecutive points and from polar coordinates system. Experiments on classification of the resulted vectors were carried out by means of Dynamic Time Warping algorithm using window and slope constraints. A brief comparison between the authors' work and other known signature techniques is also discussed in the paper.

References

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K. Saeed, M. Tabedzki, “A New Hybrid System for Recognition of Handwritten-Script,“ International Scientific Journal of Computing, Institute of Computer Information Technologies, vol. 3, no 1, Ternopil, Ukraine 2004, pp. 50-57.

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K. Saeed, M. Adamski, “Offline signature classification with DTW application,” XIV Conference on Informatics Systems - KBIB'05 (in Polish), vol. 1, pp. 455-460.

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K. Saeed, “Efficient Method for On-Line Signature Verification,” Proceedings of the International Conference on Computer Vision and Graphics - ICCVG'02, vol. 2, Zakopane 2002, pp. 25-29.

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K. Saeed, M. Adamski, “Offline signature classification with DTW application,” XIV Conference on Informatics Systems - KBIB'05 (in Polish), vol. 1, pp. 455-460.

M. Adamski, K. Saeed, “Signature identification by view-based feature extraction and Dynamic Time Warping classifier”, accepted for publication in ACS, 13th International Multi Conference, October 2006.

Adamski M., Saeed K.: Classification of handwritten signatures based on boundary tracing. 4th Intern. Conf. on Neural Networks and Artificial Intelligence - ICNNAI’06, Brest, Belarus, 2006, pp. 201-205.

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Published

2014-08-01

How to Cite

Adamski, M., & Saeed, K. (2014). HEURISTIC TECHNIQUES FOR HANDWRITTEN SIGNATURE CLASSIFICATION. International Journal of Computing, 5(2), 87-92. https://doi.org/10.47839/ijc.5.2.401

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Section

Articles