Signatures are frequently used for both personal authorization and verification. The signatures on numerous papers, including legal agreements and bank checks, must be verified. Offline signature verification is a type of authentication that examines the physical action of signing while measuring the characteristics of an user's handwriting. In the present paper, an offline signature verification method was developed utilising Convolutional Siamese Network of Deep Learning [4]. Convolutional Siamese networks, which are dual networks that shares parameters, can be developed to acquire a feature map. Thus, Convolutional Siamese network is employed to verify the person's signature to a input signature that is saved in the database. Research has been conducted with a dataset of signatures that includes 750 signatures from 30 different individuals. An Accuracy of 91.6% was attained by the proposed solution.