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)


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.


Signature classification Gradient magnitude Corner point k-NN classifier 


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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

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