Off-line handwritten signature verification

Praveena, T.; Kokul, T.

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dc.contributor.author	Praveena, T.
dc.contributor.author	Kokul, T.
dc.date.accessioned	2021-03-26T05:38:36Z
dc.date.accessioned	2022-07-07T05:07:00Z
dc.date.available	2021-03-26T05:38:36Z
dc.date.available	2022-07-07T05:07:00Z
dc.date.issued	2020
dc.identifier.uri	http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/2145
dc.description.abstract	Off-line handwritten signature is broadly used for personal identification in financial, commercial and legal document bindings. The automatic verification of human handwritten signature is a key research area with respect to improve the verification of forged signature and to reduce the crimes. The objective of this research is to provide a fast, reliable, and easy method to verify off-line handwritten signatures. Image processing techniques and Artificial Neural Network (ANN) are used in this research to achieve a better performance. This research is evaluated on a benchmark dataset , which contains 24 people,s signatures. five genunie and five fOff-line handwritten signature is broadly used for personal identification in financial, commercial and legal document bindings. The automatic verification of human handwritten signature is a key research area with respect to improve the verification of forged signature and to reduce the crimes. The objective of this research is to provide a fast, reliable, and easy method to verify off-line handwritten signatures. Image processing techniques and Artificial Neural Network (ANN) are used in this research to achieve a better performance. This research is evaluated on a benchmark	en_US
dc.language.iso	en	en_US
dc.publisher	University of Jaffna	en_US
dc.subject	signature verification	en_US
dc.subject	speed-up robust features,	en_US
dc.subject	artificial neural network	en_US
dc.title	Off-line handwritten signature verification	en_US
dc.type	Article	en_US