Advancing Handwritten Signature Verification Through Deep Learning: A Comprehensive Study and High-Precision Approach

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 forgerydetection. 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