Online Signature Verification (OSV) systems are crucial for secure authentication in digital environments, where handwritten signatures are electronically captured and verified. These systems use machine learning and deep learning methods to analyze unique dynamic features of a signature, such as pen pressure, speed, and stroke order, in addition to its static shape. Recent approaches leverage techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs) and hybrid models, to analyze the unique dynamic and static features of signatures. However, there exists a problem of designing real-time systems that are responsive, and have reliable performance using the mentioned deep learning techniques because these models have to be trained each time a new user is added to the database. The objective of our experiment is to build an OSV system that integrates a CNN-based Siamese Network and a webpage created using ReactJS to allow the user to upload their signature or store it in the database. The model extracts spatial features from the signatures and makes decisions based on the similarity of the uploaded signature with the original signature of the user. The system is designed to handle real and forged signatures through continuous model training. Using a combination of signature preprocessing techniques, feature extraction, and classification models, this system aims to ensure a robust, reliable, and secure method for identity verification in online transactions and sensitive digital applications,