SIGNATURE RECOGNITION SYSTEM USING MACHINE LEARNING AND PYTHON

Abstract

Each individual has a distinctive signature that is primarily used for personal identification and the
confirmation of significant papers or legal transactions. Static (offline) and dynamic signature verification come
in two flavour (online). After a signature has been made, it can be verified using a method known as static
verification. For a lot of documents, off line signature verification is ineffective and slow. Online biometric
personal verification, such as fingerprints, eye scans, etc., has increased in recent years as a way to get over the
limitations of offline signature verification. Convolution neural network (CNN)-based offline signature
verification is proposed in this study. We can extract more accurate representations of the image content using a
neural network model called CNN. In order to improve categorization, CNN starts with the raw pixel data from
the image, trains the model, and then automatically extracts the features. CNN\'s key advantage over its
forerunners is that it automatically identifies significant features without human supervision and that it predicts
images with the highest degree of accuracy of any algorithm.