Hand Written Signature Recognition & Verification using Neural Network

the signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. A number of biometric techniques have been proposed for personal identification in the past. Among the vision-based ones are voice recognition, face recognition, fingerprint recognition, iris scanning and retina scanning. Voice recognition or signature verification are the most widely known among the non-vision based ones. As signatures continue to play a very important role in financial, commercial and legal transactions, truly secured authentication becomes more and more crucial. Handwritten signatures are considered as the most natural method of authenticating a person's identity. A signature by an authorized person is considered to be the "seal of approval" and remains the most preferred means of authentication. 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. The method presented in this paper consists of image prepossessing, geometric feature extraction, neural network training with extracted features and verification. A verification stage includes applying the extracted features of test signature to a trained neural network which will classify it as a genuine or forged. In this paper, off- line signature recognition & verification using neural network is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified based on parameters extracted from the signature using various image processing techniques. The Off-line Signature Recognition and Verification is implemented using MATLAB. This work has been tested and found suitable for its purpose.

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