Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/2145
Title: Off-line handwritten signature verification
Authors: Praveena, T.
Kokul, T.
Keywords: signature verification;speed-up robust features,;artificial neural network
Issue Date: 2020
Publisher: University of Jaffna
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
URI: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/2145
Appears in Collections:FARS 2020

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