Signature Verification using ResNet-50 Model

Abstract

Signed documents are widely accepted as a means of confirming identification, which offers signature verification systems a major advantage over other kinds of technologies. There are two types of approaches to solving this issue using a signature verification system: online and offline. Offline signature verification uses less electronic administration and uses recorded signature images from a camera or scanner. An offline signature verification method uses extracted features from the scanned signature image. This study's primary contribution is the understanding of how deep learning network ResNet-50 can be applied to offline signature verification systems. This paper proposes the use of ResNet-50 for offline signature verification. One kind of pretrained model that enables us to extract higher representations for the image content is called ResNet-50. CNN trained the model using the raw pixel data from the image, then automatically extracted the features for improved categorization. ResNet-50's primary advantage over its predecessors is that it has the highest accuracy of all image prediction algorithms and can automatically identify essential characteristics without human supervision. The accuracy of the ResNet-50 model was 75.8%, indicating good performance.