Advancing Handwritten Signature Verification Through Deep Learning: A Comprehensive Study and High-Precision Approach
Advancing Handwritten Signature Verification Through Deep Learning: A Comprehensive Study and High-Precision Approach |
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| © 2024 by IJETT Journal | ||
| Volume-72 Issue-4 |
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| Year of Publication : 2024 | ||
| Author : Abdullahi Ahmed Abdirahma, Abdirahman Osman Hashi, Mohamed Abdirahman Elmi, Octavio Ernest Romo Rodriguez |
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| DOI : 10.14445/22315381/IJETT-V72I4P109 | ||
How to Cite?
Abdullahi Ahmed Abdirahma, Abdirahman Osman Hashi, Mohamed Abdirahman Elmi, Octavio Ernest Romo Rodriguez, "Advancing Handwritten Signature Verification Through Deep Learning: A Comprehensive Study and High-Precision Approach," International Journal of Engineering Trends and Technology, vol. 72, no. 4, pp. 81-91, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I4P109
Abstract
This paper presents a comprehensive study on handwritten signature verification using deep learning techniques. This research aims to address the challenges of offline signature verification, where the task is to distinguish genuine signatures from forgeries automatically. The proposed method utilizes state-of-the-art deep learning models, including MobileNet, ResNet50, Inceptionv3, and VGG19, in combination with YOLOv5, to achieve high-precision classification and reliable forgery detection. The system is evaluated on multiple benchmark datasets, including Kaggle Signature, CEDAR, ICDAR, and Sigcomp, showcasing its effectiveness and robustness across various real-world scenarios. The proposed methodology encompasses data preprocessing techniques to enhance the quality of input handwritten signature images, enabling the model to capture essential features and patterns for accurate classification. The results demonstrate the superiority of the proposed method compared to existing state-of-the-art
approaches, achieving outstanding accuracy rates (89.8%) in identifying genuine signatures and accurately detecting forgeries. Furthermore, the model's adaptability to varying dataset sizes and configurations further supports its potential for practical deployment in signature verification tasks. This research contributes to the advancement of offline signature verification technology, offering a reliable and efficient solution for ensuring the security and authenticity of handwritten signatures in a variety of applications.
Keywords
Offline signature verification, Deep Learning, Handwrite signature, Signature recognition, YOLOv5.
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