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Abstract

Handwritten signature identification is the process of determining an individual’strue identity by analyzing their signature. This is an important task in various applications such as financial transactions, legal documentverification, and biometric systems. However, verifying handwritten signatures is challenging even in the age of digital transactions and remote document authentication. The inherent variety in people’s signatures, which may occurdue to factors such as mood, exhaustion, or even the writing tool used, contributes to the problem. Furthermore, the proliferation of sophisticated forgery methods, such as freehand mimicking and sophisticated picture manipulation, necessitates the development of reliable and precise tools for identifying authentic signatures from fake ones. Various techniques have been developed for signature identification, including feature-based and machine learning-based methods. This paper proposes an authentic signature identification method based on integrating static (offline) signature data and a deep-based model, which fusesthree types of signature features—Linear Discriminant Analysis as appearance-based features, Fast Fourier Transform as frequency-based features, and Grey-Level Co-occurrence Matrix as texture-based features. Thefused features are then fed into the proposed deep-based model of 25 layers to identify each person. For experiments, we employedthree datasets: our private collected dataset, called SigArab, and two public datasets called SigComp2011 and CEDAR. The proposed deep model achieved99.23%, 100%, and 100% accuracy on the SigArab, CEDAR, and SigComp2011 datasets, respectively. In terms of precision, recall, and F-score, the findings revealed positiveresults for both datasets and exceeded 1.00, 0.487, and 0.655, respectively, on Sigcomp2011 dataset and 1.00, 0.507, and 0.672, respectively, on CEDAR dataset

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