A Survey : Deep Learning Approaches for Signature Verification

Authors

  • Deepali Narwade  Department of Computer Engineering, D. Y. Patil College of Engineering, Akurdi, Pune, Maharashtra, India
  • Vaishali Kolhe  Department of Computer Engineering, D. Y. Patil College of Engineering, Akurdi, Pune, Maharashtra, India

DOI:

https://doi.org/10.32628/IJSRSET23103216

Keywords:

Deep Learning, Signature Verification, RNNs, CNNs.

Abstract

Since a person's signature serves as the primary authentication and authorization method in legal transactions, there is a greater need than ever for effective auto-mated signature verification solutions. The fact that signatures are already recognized as a popular way of identity verification gives signature verification systems a significant edge over other types of technologies. The methods used to solve this problem and a signature verification system can be categorized into two categories: online and offline. An electronic tablet and pen that are connected to a computer are used in the online technique to extract information about a signature and collect dynamic data for verification purposes, such as pressure, velocity, and writing speed. Offline signature verification, on the other hand, employs signature images that have been recorded by a scanner or camera and involve less electronic management. Extracted features taken from the scanned signature image are used in an offline signature verification system. The main contribution of this study is how we can use deep learning approaches/networks for the task of offline signature verification systems.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2023-08-30

Issue

Volume 10, Issue 4, July-August-2023

Section

Research Articles

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How to Cite

[1]
Deepali Narwade, Vaishali Kolhe "A Survey : Deep Learning Approaches for Signature Verification" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 10, Issue 4, pp.291-312, July-August-2023. Available at doi : https://doi.org/10.32628/IJSRSET23103216