Signature Verification System Using CNN

A Signature Verification System using Convolutional Neural Networks (CNN) leverages deep learning techniques to accurately authenticate signatures by analyzing their unique patterns and features. The process begins with collecting a dataset of genuine and forged signatures, which are then preprocessed to ensure uniformity in size and quality. The CNN architecture consists of multiple convolutional layers that extract relevant features from the signature images, followed by pooling layers that reduce dimensionality while preserving important information. After training the model on this dataset, it learns to distinguish between authentic and counterfeit signatures based on the learned features. The system evaluates new signatures by passing them through the trained CNN, yielding a probability score that indicates authenticity. This approach offers enhanced accuracy and robustness compared to traditional methods, making it a valuable tool in various applications, such as banking and legal documentation, where signature verification is critical.