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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM17704

SIGNATURE FORGERY RECOGNITION

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM17704

Volume:

7 |

2 |

Feb 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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.

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Authors

SB

Shriganesh Bhandari

SS

Satyam Shukla

OK

Omkar Koyande

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Abstract

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

Offline Signature Recognition Siamese Neural Network Contrastive loss Euclidean Distance.

Cite This Article

Shriganesh Bhandari, Satyam Shukla, Omkar Koyande, "SIGNATURE FORGERY RECOGNITION," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 2, Feb 2023. https://doi.org/10.55041/IJSREM17704

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Publication Date

8/1/2023

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Double-Blind Peer Review

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