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 |
Files in This Item:
File | Description | Size | Format | |
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OFF-LINE HANDWRITTEN SIGNATURE VERIFICATION.pdf | 281.71 kB | Adobe PDF | View/Open |
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