CONTRIBUTION TO THE AUTHENTICITY OF DIGITIZED HANDWRITTEN SIGNATURES THROUGH DEEP LEARNING WITH RESNET-50 AND OCR

Authors

  • Tsanta Christelle Nadège Ralaibozaka Ecole supérieure de Polytechnique d’Antananarivo, University of Antananarivo, Antananarivo, Madagascar
  • Maminiaina Alphonse Rafidison Ecole supérieure de Polytechnique d’Antananarivo, University of Antananarivo, Antananarivo, Madagascar
  • Hajasoa Malalatiana Ramafiarisona Ecole supérieure de Polytechnique d’Antananarivo, University of Antananarivo, Antananarivo, Madagascar

DOI:

https://doi.org/10.26662/ijiert.v11i3.pp20-25

Keywords:

circumvention techniques, signature styles, adaptability

Abstract

This paper explores the contribution of authenticity to digitized handwritten signatures using a deep learning-based approach, implementing ResNet-50 and optical character recognition (OCR). Signature authentication is a crucial issue in various fields, such as transaction security, protection of official documents, and fraud prevention. Our approach aims to improve the reliability of signature verification systems by exploiting the advanced capabilities of deep neural networks. Experimental results demonstrate a high authentication accuracy of 94% on our collected database and 100% on ICDAR 2011, validating the effectiveness of the proposed approach. The advantages of this method include more excellent resistance to circumvention techniques, adaptability to different signature styles, and robustness against intentional tampering.

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Published

2024-03-27

Issue

Vol. 11 No. 3 (2024): IJIERT

Section

Engineering and Technology

License

Copyright (c) 2024 International Journal of Innovations in Engineering Research and Technology

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