A Survey of Digitized Handwritten Signature Verification System

In this digital world, the problem of recognizing a person is resolved by various biometric applications. Along with various unique physical biometric patterns like face, iris, fingerprints, etc., some of the behavioural biometric patterns like signatures, voice, gait, etc. are also used for recognizing an individual. Among all these, handwritten signatures play a major role in our daily life. In this paper, we have discussed the recent work done on online and offline modes which are the two ways of digitizing the handwritten signatures. We have done a comparison of these works based on various features and techniques used. The raw features such as the image of signature and position coordinates, time, pressure, etc. are considered in offline and online signature verification, respectively. For matching the signature pattern, many Artificial-Intelligence-based techniques and machine learning algorithms like support vector machine, hidden Markov model, neural network, fuzzy logic, deep learning, etc. have been used in developing different handwritten signature verification systems. We have also highlighted some signature verification systems which considered handwritten signatures of different languages like Arabic, Chinese, Japanese, Bangla, Hindi, English, etc. The performances of such signature verification systems lead us to do more work using other Indic-scripts-based signatures.