In this study, a hybrid signature verification system has been developed to prevent forgery by using both online and offline signature data. Signature is considered an important type of biometric data for individual authentication and document validation. With increasing digitalization, traditional signature verification methods have become time-consuming, error-prone, and inadequate in terms of security. The proposed system is based on image processing techniques such as gray scaling, thresholding, edge detection, and contour analysis for offline signature verification; and analysis of dynamic features such as velocity, acceleration, orientation, timing, and motion path for online signature verification. The developed structure ensures data security through an API-supported infrastructure, transmitting signature data to a central server and automating analysis processes. The verification process is carried out by comparing both new and signatures from different sources using a reference database created according to signature data obtained from the user. The system has been made suitable for individuals of different ages and experience levels with a user-friendly interface design and aims to provide an applicable solution in sectors where forgery prevention is critical, such as banking, finance, healthcare, and security. Future studies aim to improve the system's performance with larger datasets and different device integrations
Akalın et al. (Tue,) studied this question.