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Feature Learning for Offline Handwritten Signature Verification Using Convolutional Neural Network

Author

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  • Amruta Bharat Jagtap

    (Solapur University, Solapur, India)

  • Ravindra S. Hegadi

    (Solapur University, Solapur, India)

  • K.C. Santosh

    (University of South Dakota, Vermillion, USA)

Abstract

In biometrics, handwritten signature verification can be considered as an important topic. In this article, the authors' proposed method to verify handwritten signatures are based on deep convolution neural network (CNN), which is s bio-inspired network that works as if there exists human brain. Deep CNN extracts features from the studied images, which is followed by cubic support vector machine for classification. To evaluate their proposed work, the authors have tested on three different datasets: GPDS, BME2 and SVC20, and have received encouraging results.

Suggested Citation

  • Amruta Bharat Jagtap & Ravindra S. Hegadi & K.C. Santosh, 2019. "Feature Learning for Offline Handwritten Signature Verification Using Convolutional Neural Network," International Journal of Technology and Human Interaction (IJTHI), IGI Global, vol. 15(4), pages 54-62, October.
    • Handle: RePEc:igg:jthi00:v:15:y:2019:i:4:p:54-62
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