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Feature Learning for Offline Handwritten Signature Verification Using Convolutional Neural Network

Amruta Bharat Jagtap, Ravindra S. Hegadi, K.C. Santosh

Source Title: International Journal of Technology and Human Interaction (IJTHI)15(4)

ISSN: 1548-3908|EISSN: 1548-3916|EISBN13: 9781522564171|DOI: 10.4018/IJTHI.2019100105

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MLA

Jagtap, Amruta Bharat, et al. "Feature Learning for Offline Handwritten Signature Verification Using Convolutional Neural Network." IJTHI vol.15, no.4 2019: pp.54-62. http://doi.org/10.4018/IJTHI.2019100105

APA

Jagtap, A. B., Hegadi, R. S., & Santosh, K. (2019). Feature Learning for Offline Handwritten Signature Verification Using Convolutional Neural Network. International Journal of Technology and Human Interaction (IJTHI), 15(4), 54-62. http://doi.org/10.4018/IJTHI.2019100105

Chicago

Jagtap, Amruta Bharat, Ravindra S. Hegadi, and K.C. Santosh. "Feature Learning for Offline Handwritten Signature Verification Using Convolutional Neural Network," International Journal of Technology and Human Interaction (IJTHI) 15, no.4: 54-62. http://doi.org/10.4018/IJTHI.2019100105

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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.

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Feature Learning for Offline Handwritten Signature Verification Using Convolutional Neural Network

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