Signature Verification Using CNN with Information Retrieval

Authors(4) :-Utkarsh Shukla, Srishti Verma, Tushar Gwal, Atul Kumar Verma

When it comes to information security, biometric systems play a significant role in it. Signature verification is a popular research area in field of pattern recognition and image processing. It is a technique used by banks, intelligence agencies and high-profile institutions to validate the identity of an individual by comparing signatures and checking for authenticity. In this paper, the approach for the verification of signatures is based on Conventional Neural Network (CNN). This method saves time and energy and also helps to prevent human error during the signature process and lowers chances of fraud in the process of authentication. We achieved test accuracy of 89% and validation accuracy of 93%.

Authors and Affiliations

Utkarsh Shukla
Department of Computer Science and Engineering, Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, Uttar Pradesh, India
Srishti Verma
Department of Computer Science and Engineering, Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, Uttar Pradesh, India
Tushar Gwal
Department of Computer Science and Engineering, Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, Uttar Pradesh, India
Atul Kumar Verma
Department of Computer Science and Engineering, Shri Ramswaroop Memorial College of Engineering and Management, Lucknow, Uttar Pradesh, India

Signature Verification, Pattern Recognition, Image Processing, CNN

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Publication Details

Published in : Volume 3 | Issue 7 | September-October 2018
Date of Publication : 2018-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 314-319
Manuscript Number : CSEIT183756
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Utkarsh Shukla, Srishti Verma, Tushar Gwal, Atul Kumar Verma, "Signature Verification Using CNN with Information Retrieval", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 7, pp.314-319, September-October-2018.
Journal URL : http://ijsrcseit.com/CSEIT183756

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