Cover
Vol. 20 No. 2 (2024)

Published: December 31, 2024

Pages: 77-84

Original Article

Handwritten Signature Verification Method Using Convolutional Neural Network

Wijdan Yassen A. AlKarem 1 Eman Thabet Khalid Khawla. H. Ali
1Department of Computer Sciences, Education College for Pure Sciences, University of Basrah, Basrah, Iraq Correspondance *Wijdan Yassen A. AlKarem Department of Computer Sciences, Education College for Pure Sciences, University of Basrah, Basrah, Iraq. Email: wijdan.abdalkarem@uobasrah.edu.iq
History
  • Received: August 20, 2023
  • Revised: September 10, 2023
  • Accepted: September 14, 2023
  • Available Online: November 30, 2024
DOI

https://doi.org/10.37917/ijeee.20.2.7

Abstract

Automatic signature verification methods play a significant role in providing a secure and authenticated handwritten signature in many applications, to prevent forgery problems, specifically institutions of finance, and transections of legal papers, etc. There are two types of handwritten signature verification methods: online verification (dynamic) and offline verification (static) methods. Besides, signature verification approaches can be categorized into two styles: writer dependent (WD), and writer independent (WI) styles. Offline signature verification methods demands a high representation features for the signature image. However, lots of studies have been proposed for WI offline signature verification. Yet, there is necessity to improve the overall accuracy measurements. Therefore, a proved solution in this paper is depended on deep learning via convolutional neural network (CNN) for signature verification and optimize the overall accuracy measurements. The introduced model is trained on English signature dataset. For model evaluation, the deployed model is utilized to make predictions on new data of Arabic signature dataset to classify whether the signature is real or forged. The overall obtained accuracy is 95.36% based on validation dataset.

Keywords

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