Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/2145
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dc.contributor.authorPraveena, T.
dc.contributor.authorKokul, T.
dc.date.accessioned2021-03-26T05:38:36Z
dc.date.accessioned2022-07-07T05:07:00Z-
dc.date.available2021-03-26T05:38:36Z
dc.date.available2022-07-07T05:07:00Z-
dc.date.issued2020
dc.identifier.urihttp://repo.lib.jfn.ac.lk/ujrr/handle/123456789/2145-
dc.description.abstractOff-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 benchmarken_US
dc.language.isoenen_US
dc.publisherUniversity of Jaffnaen_US
dc.subjectsignature verificationen_US
dc.subjectspeed-up robust features,en_US
dc.subjectartificial neural networken_US
dc.titleOff-line handwritten signature verificationen_US
dc.typeArticleen_US
Appears in Collections:FARS 2020

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