Abstract
This paper presents a Java–Python-based platform for signature verification which is able to extract features from an individual’s signatures and discriminate genuine signatures from forgeries. Feature study, algorithm development, challenges with training a neural network, and its design solutions are presented in the paper. The proposed model uses global, statistical, and local features of the input dataset. Experimental results suggest that it is able to deliver 95 % accuracy.
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© 2016 Springer Science+Business Media Singapore
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Savita Choudhary, Sridhar Mishra, Siddanth Kaul, Arun, J.B. (2016). Design of Handwritten Signature Verification Using Java–Python Platform. In: Shetty, N., Prasad, N., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications . Springer, Singapore. https://doi.org/10.1007/978-981-10-0287-8_7
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DOI: https://doi.org/10.1007/978-981-10-0287-8_7
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