ARTIFICIAL INTELLIGENCE-BASED SIGNATURE RECOGNITION TECHNOLOGIES

Abstract

This paper explores artificial intelligence-based technologies for signature recognition, which is a vital method of biometric verification. Signatures remain a widely accepted and legally binding form of personal authentication. However, manual or visual verification is prone to error and forgery. This study proposes a deep learning model based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to identify and verify handwritten signatures from scanned or digitally captured input. The model processes spatial and temporal features to distinguish between genuine and forged signatures. The results show that AI can achieve high levels of accuracy, making it viable for use in banking, digital contracts, and legal procedures.