Digital Signature Verification System Using Convolutional Neural Networks
Aman Kumar Mishra, Dr.Shivani Dubey, Prof.Vikas Singhal, Dr.Pankaj Kumar Gupta · International Journal of Innovative Research in Advanced Engineering · 2024
The design, development, and evaluation of a signature verification system, a critical component of biometric authentication. The study employs two primary datasets, the MCYT (MasterCard Young Teenager) Signature Dataset and the GPDS (Greek Sign Language Recognition) Signature Dataset, to assess the system's adaptability and accuracy. The MCYT dataset, featuring 6600 signature samples, provides variability in writing styles and demographic information for potential nuanced analyses. In contrast, the GPDS dataset, comprising 4000 signatures, introduces dynamic signing variations during Greek Sign Language communication. The project aims to develop a robust signature verification system capable of handling diverse scenarios, ultimately contributing to the advancement of biometric technology. The research combines insights from real-world variability and linguistic nuances, offering a comprehensive understanding of the system's effectiveness.