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Monthly Journal: Publishes theoretical and applied research in all areas of Discrete Mathematical Sciences, Cryptography, Combinatorics, Elliptic Curves and Information Security.

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Open Access Research Article

Development of signature recognition system using VGG16

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pp. 807–813Vol. 26Issue 3April 2023DOI: 10.47974/JDMSC-1756 Crossmark XML
Published Online:
01 Apr 2023
Article type:
Research Article
Language:
EN
Article no.:
JDMSC-1756
Pages:
807–813

Abstract

The goal of this research is to locate a signature recognition system that can replace individual presence in the process of handwritten signature recognition. It has been discovered that an automatic signature recognition system that does not require a person’s physical presence is necessary throughout corona duration. In this article more accurate signature recognition model employing VGG 16 pre- trained models is proposed. Novel Convolution neural network has exhibited 83% validation accuracy on the GPDS synthetic Signature dataset[1].

Keywords

Subject Classifications

68T07 Artificial neural networks and deep learning

References

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