Handwritten signature classification stands as a fundamental aspect in document verification systems. The paper develops a stable handwritten signature classification framework based on Convolutional Neural Networks (CNN), the lightweight Mobile Net architecture and Alexnet for optimizing signature verification precision and operation speed. Handwritten signature classification represents an intricate process because writing styles consistently exhibit distinct individual characteristics hence requiring systems with sophisticated understanding capabilities. The model design implements CNN extraction of hierarchical image features and Mobile Net structure to achieve both performance excellence and operational compatibility across devices independent of their computing power. The present approach applies data augmentation together with transfer learning methods which improve both the generalization performance and unseen data accuracy of the model. Breeding from our signature classification framework yields promising outcome.
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