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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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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: Rajesh Babu, rajeshbabud.cse@srit.ac.in
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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