International Journal of Science, Engineering and TechnologyAuthors: Shaik Nakarikanti Shabana, P. Anusha
Abstract: Secure authentication in digital settings that electronically collect and verify handwritten signatures relies heavily on Online Signature Verification (OSV) technologies. These systems analyse not only the static structure of a signature but also its unique dynamic properties like pen pressure, pace, and stroke order using machine learning and deep learning methodologies. Hybrid models, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generator Adversarial Networks (GANs) are some of the current methods used to examine the distinct static and dynamic aspects of signatures. The above deep learning approaches provide a challenge when it comes to developing responsive and dependable real-time systems. This is because these models need to be trained every time a new user is added to the database. Our experiment's goal is to construct an OSV system that comprises a ReactJS-built website for signature uploading or database storage and a CNN-based Siamese Network for OSV integration. The model uses the spatial information extracted from the signatures to make judgements depending on how close the uploaded signature is to the user's original signature. Through ongoing model training, the system is prepared to deal with both genuine and fake signatures. This system is designed to provide a safe, dependable, and resilient way to verify identification in online transactions and sensitive digital applications. It does this by combining signature preprocessing methods, feature extraction, and classification models. Things like online signature verification, siamese networks, react-js, and feature extraction are all part of the index.
DOI: http://doi.org/10.5281/zenodo.20271121
