Offline Signature Verification System Using CNN

One of the challenging and effective ways of identifying a person through biometric techniques is Signature verification as compared to the traditional handcrafted system, where a forger has access and also attempts to imitate it which is used in commercial scenarios, like bank check payment, business organizations, educational institutions, government sectors, health care industry etc. so the signature verification process is used for human examination of a single known sample. There are mainly two types of signature verification: static and dynamic. i) Static or offline verification is the process of verifying an electronic or document signature after it has been made, ii) Dynamic or online verification takes place as a person creates his/her signature on a digital tablet or a similar device. Compared, Offline signature verification is not efficient and slow for a large number of documents. Therefore, although vast and extensive research on signature verification there is a need to more focus on and review the online signature verification method to increase efficiency using deep learning. In this project, we achieve 94.58% accuracy using a convolutional neural network.