  Signature verification has been one of the major researched areas in the
field of computer vision. Many financial and legal organizations use signature
verification as access control and authentication. Signature images are not
rich in texture; however, they have much vital geometrical information. Through
this work, we have proposed a signature verification methodology that is simple
yet effective. The technique presented in this paper harnesses the geometrical
features of a signature image like center, isolated points, connected
components, etc., and with the power of Artificial Neural Network (ANN)
classifier, classifies the signature image based on their geometrical features.
Publicly available dataset MCYT, BHSig260 (contains the image of two regional
languages Bengali and Hindi) has been used in this paper to test the
effectiveness of the proposed method. We have received a lower Equal Error Rate
(EER) on MCYT 100 dataset and higher accuracy on the BHSig260 dataset.
