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 Field | Value | Language |
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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 |
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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