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Signature Recognition and Verification Using Zonewise Statistical Features

  • Banashankaramma F. Lakkannavar
  • M. M. Kodabagi
  • Susen P. NaikEmail author
Conference paper
  • 35 Downloads
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 49)

Abstract

The signature is defined as special kind of handwriting that includes special characters and flourishes. Handwritten signatures are most accepted individual attribute for individuality authentication of the person. This provides novel method for the signature recognition and verification by using zone based statistical features. It contains mainly two phases. During the first phase, the knowledge base is constructed by training samples using the zone wise statistical features. During second stage i.e., testing phase, the processed image is obtained having zoning wise statistical features and signature is recognized using neural network classifiers. An accuracy rate of 97.5% is achieved by testing 200 samples. MATLAB is used for designing this signature recognition and verification system and is robust and avoids noise, blur and change in size, lightening conditions and other possible degradation.

Keywords

Signature recognition and verification Signature images Zone wise statistical features Neural network classifier 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Banashankaramma F. Lakkannavar
    • 1
  • M. M. Kodabagi
    • 2
  • Susen P. Naik
    • 3
    Email author
  1. 1.CSE DepartmentGovernment PolytechnicBagalkotIndia
  2. 2.Department of Computer Science and EngineeringReva UniversityBengaluruIndia
  3. 3.Department of Computer ScienceK.L.E. Institute of TechnologyHubliIndia

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