Signature Recognition Using Machine Learning

Abstract

Signatures are popularly used as a method of personal identification and confirmation. Many certificates such as bank checks and legal activities need signature verification. Verifying the signature of a large number of documents is a very difficult and time-consuming task. As a result, explosive growth has been observed in biometric personal verification and authentication systems that relate to unique quantifiable physical properties (fingerprints, hand, and face, ear, iris, or DNA scan) or behavioral characteristics (gait, sound, etc.). Several methods are used to describe the ability of the suggested system in specifying the genuine signatures from the forgeries. This approach presents a new technique for signature verification and recognition, using a tow dataset for training the model by a siamese network.