Published Paper Details:

Every person has his/her own unique signature that is used mainly for the purposes of personal identification and verification of important documents or legal transactions. There are two kinds of signature verification: static(offline) and dynamic(online). Static verification is the process of verifying an electronic or document signature after it has been made. Offline signature verification is not efficient and slow for a large number of documents. To overcome the drawbacks of offline signature verification, we have seen a growth in online biometric personal verification such as fingerprints, eye scan etc. In this project , offline signature verification using Convolutional Neural Network (CNN) is proposed. CNN is a type of neural network model which allows us to extract higher representations for the image content. CNN takes the image's raw pixel data, trains the model, then extracts the features automatically for better classification . The main advantage of CNN compared to its predecessors is that it automatically detects the important features without any human supervision also it has the highest accuracy among all algorithms that predicts images.