Signature Recognition & Verification System Using Back Propagation Neural Network
Nilesh Choudhary, Bhupendra M. Chaudhari · 2013
The fact that the signature is widely used as a means of personal identification tool for humans require that the need for an automatic verification system. Verifwication can be performed either Offline or Online based on the application. However human signatures can be handled as an image and recognized using computer vision and neural network techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are various approaches to signature recognition with a lot of scope of research. In this paper, off-line signature recognition & verification using back propagation neural network is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified based on features extracted from the signature using Invariant Central Moment and Modified Zernike moment for its invariant feature extraction because the signatures are Hampered by the large amount of variation in size, translation and rotation and shearing parameter. Before extracting the features, preprocessing of a scanned image is necessary to isolate the signature part and to remove any spurious noise present. The system is initially trained using a database of 56 persons signatures obtained from those 56 individuals whose signatures have to be authenticated by the system. For each subject a mean signature is obtained integrating the above features derived from a set of his/her genuine sample signatures.This signature recognition& verification system is designed using MATLAB. This work has been tested and found suitable for its purpose.