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Handwritten signature verification poses a formidable challenge in biometricsand document authenticity. The objective is to ascertain the authenticity of aprovided handwritten signature, distinguishing between genuine and forged ones.This issue has many applications in sectors such as finance, legaldocumentation, and security. Currently, the field of computer vision andmachine learning has made significant progress in the domain of handwrittensignature verification. The outcomes, however, may be enhanced depending on theacquired findings, the structure of the datasets, and the used models. Fourstages make up our suggested strategy. First, we collected a large dataset of12600 images from 420 distinct individuals, and each individual has 30signatures of a certain kind (All authors signatures are genuine). In thesubsequent stage, the best features from each image were extracted using a deeplearning model named MobileNetV2. During the feature selection step, threeselectors neighborhood component analysis (NCA), Chi2, and mutual info (MI)were used to pull out 200, 300, 400, and 500 features, giving a total of 12feature vectors. Finally, 12 results have been obtained by applying machinelearning techniques such as SVM with kernels (rbf, poly, and linear), KNN, DT,Linear Discriminant Analysis, and Naive Bayes. Without employing featureselection techniques, our suggested offline signature verification achieved aclassification accuracy of 91.3%, whereas using the NCA feature selectionapproach with just 300 features it achieved a classification accuracy of 97.7%.High classification accuracy was achieved using the designed and suggestedmodel, which also has the benefit of being a self-organized framework.Consequently, using the optimum minimally chosen features, the proposed methodcould identify the best model performance and result validation predictionvectors.

Offline Handwriting Signature Verification: A Transfer Learning and Feature Selection Approach