In a variety of contexts, including banking, legal documents, and financial transactions, signature verification is essential to identity authentication. Conventional signature verification techniques depend on manual inspection, which is frequently laborious and prone to human error. Deep learning methods have become viable options for automated signature verification with the development of artificial intelligence. The extensive learning-based online signature verification method presented in this work examines the dynamic and visual characteristics of signatures to ascertain their legitimacy. After processing input signatures, the system determines whether they are authentic or fraudulent by identifying key characteristics. To enable users to upload signatures and obtain real-time verification results, a web-based interface is created. The suggested approach has great reliability and precision in differentiating between real and fake signatures, according to experimental data. For safe authorization apps, the system offers a scalable and effective solution.
Mahesh et al. (Sun,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: