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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM17704
Volume:
7 |
2 |
Feb 2023
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Signatures are one of the most important techniques for biometric authentication. There are two kinds of signature nowadays, offline(static) and dynamic (online). There are greater distinctive characteristics of offline signatures, but there are less distinctive characteristics of offline signatures. Offline signatures are also harder to check. Furthermore, the most significant downside of offline signatures is that even the most talented signer does not sign the same way. This is called Intra-personal variability. Both checking the offline signatures is a difficult problem for researchers. In this research, we proposed an offline signature verification approach based on the Deep Learning to prevent signature fraud by malicious individuals.
KEYWORDS
Publication Date
8/1/2023
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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