Abstract
Automatic signature extraction and recognition from document images is an open research problem. Signature verification is of two types; static and dynamic, and has two approaches; writer dependent and writer independent. Signature verification system in case of bank cheque image should essentially be an error prone system to elude the fraudulent transactions. In this work, a three layer signature verification system is proposed, which is writer independent and offline signature verification system. Graphometrical and FAST features are extracted from the signature images and are given as inputs to the classification algorithms. The proposed signature verification model is a combination of three classification algorithms; artificial neural network, Gaussian mixture model and image matching models, to circumvent the fraudulent transactions. The overall performance accuracy of proposed process is 99%.
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