Advertisement

Geometrical Transformation Invariant Approach for Classification of Signatures Using k-NN Classifier

  • Chandrima GangulyEmail author
  • Susovan Jana
  • Ranjan Parekh
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1030)

Abstract

Signature-based authentication of human is still very popular approach. Manual checking is not always accurate and it depends on expertise. The need is an automated and accurate system for signature classification. The signatures do not necessarily comprise of well-formed letters. It can be a random combination of curves and lines. The written signature may be of variable sized, inclined in arbitrary angle or misplaced. This makes the classification task more challenging. This paper proposes an automated approach of handwritten signature classification addressing those problems. The binarized version of the input image is pre-processed in various ways to compensate translation, rotation and noise removal. The four features, which does not vary due to scaling, are selected from the pre-processed image for the classification using k-NN classifier. Overall system accuracy of the proposed approach is 92% on a dataset of 100 images.

Keywords

Signature classification Gradient magnitude Corner point k-NN classifier 

References

  1. 1.
    Ubul, K., Adler, A., Abliz, G., Yasheng, M., Hamdulla, A.: Off-line Uyghur signature recognition based on modified grid information features. In: 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA). IEEE (2012)Google Scholar
  2. 2.
    Ilmi, N., Budi, W.T.A., Nur, R.K.: Handwriting digit recognition using local binary pattern variance and K-Nearest neighbor classification. In: 4th International Conference on Information and Communication Technology (ICoICT). IEEE (2016)Google Scholar
  3. 3.
    Roy, S., Maheshkar, S.: Offline signature verification using grid based and centroid based approach. Int. J. Comput. Appl. 86 (2014)CrossRefGoogle Scholar
  4. 4.
    Rathi, A., Rathi, D., Astya, A.: Offline handwritten signature verification by using pixel based method. Int. J. Eng. Res. Technol. (IJERT) 1 (2012)Google Scholar
  5. 5.
    Jana, R., Saha, R., Dutta, D.: Offline signature verification using Euclidian distance. Int. J. Comput. Sci. Inf. Technol. 5, 707–710 (2014)Google Scholar
  6. 6.
    Marušić, T., Marušić, Ž., Šeremet, Ž.: Identification of authors of documents based on offline signature recognition. In: MIPRO, pp. 1144–1149, May 2015Google Scholar
  7. 7.
    Hiremath, G.: Verification of offline signature using local binary and directional pattern. Int. J. Innovative Sci. Eng. Technol. (IJISET) 3 (2016)Google Scholar
  8. 8.
    Panchal, T., Patel, H., Panchal, A.: License plate detection using Harris corner and character segmentation by integrated approach from an image. In: 7th International Conference on Communication, Computing and Virtualization, pp. 419–425 (2016)CrossRefGoogle Scholar
  9. 9.
    Bisen, R., Mishra, A.: Offline signature verification with random and skilled forgery detection, using grid based feature extraction. Int. J. Electron. Electr. Comput. Syst. 5 (2016)Google Scholar
  10. 10.
    Yogesh, G., Patil, A.: Offline and Online Signature Verification Systems: A Survey. Int. J. Res. Eng. Technol. 3, 328–332 (2014)CrossRefGoogle Scholar
  11. 11.
    Kanetkar, S., Pathania, A., Venugopal, V., Sundaram, S.: Offline writer identification using local derivative pattern. In: 15th International Conference on Frontiers in Handwriting Recognition. IEEE (2016)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Chandrima Ganguly
    • 1
    Email author
  • Susovan Jana
    • 2
  • Ranjan Parekh
    • 1
  1. 1.School of Education TechnologyJadavpur UniversityKolkataIndia
  2. 2.Department of Production EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations