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Verification of Handwritten Signature using Deep Learning

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dc.contributor.author Alajrami, Eman
dc.contributor.author Ashqar, Belal
dc.contributor.author Khalil, Musleh
dc.contributor.author Alaa, Barhoom
dc.date.accessioned 2020-02-14T15:50:59Z
dc.date.available 2020-02-14T15:50:59Z
dc.date.issued 2020
dc.identifier.uri http://dspace.alazhar.edu.ps/xmlui/handle/123456789/569
dc.description.abstract Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static and dynamic. Static(off-line) verification is the process of verifying an electronic or document signature after it has been made, while dynamic(on-line) verification takes place as a person creates his/her signature on a digital tablet or a similar device. Offline signature verification is not efficient and slow for a large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this paper we created CNN model using python for offline signature and after training and validating, the accuracy of testing was 99.70%. en_US
dc.language.iso en_US en_US
dc.subject Handwritten en_US
dc.subject Signature en_US
dc.subject Deep Learning en_US
dc.title Verification of Handwritten Signature using Deep Learning en_US
dc.type Article en_US


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