Advanced AI-Powered Signature Authentication System using Siamese Neural Network

Signature forgery is a major concern in high-stakes fields such as law, finance, and business, where secure authentication is censorious. This research addresses the challenges of traditional authentication of signatures using a deep learning-based system. Signature authentication remains a tenacious concern, and traditional systems where manual inspection is tender, are prone to human error and inefficiency. This research investigated deep learning techniques based on the Siamese Neural Network (SNN) model. SNN is a specialized deep learning architecture, that leverages automation and strengthens signature verification seamlessly. The SNN-based approach authorizes both skilled and random identification of signatures by comparing the samples and ensures high accuracy of signature classification. The system provides a high volume of signature verification quickly, the system is highly scalable, and also ensures authentic results through a well-praised user-friendly interface. The entire system is integrated with an SQLite database for handling a large amount of data with the SNN model for real-time assessment of the signatures.