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Signature verification systems can be categorized as offline (static) and online (dynamic). This paper presents neural network based recognition of offline signatures system that is trained with low-resolution scanned signature images using Global Feature with ACO. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. 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 neural network is proposed with Global Feature with ACO, where the signature is captured and presented to the user in an image format and with the help of Global Feature we extract total feature of signature and these features are train using neural network. 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 Image Processing and Neural Network Toolbox MATLAB Software. This work has been tested and found suitable for its purpose. |
