%0 Journal Article
	%A Laila Y. Fannas and  Ahmed Y. Ben Sasi
	%D 2013
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 82, 2013
	%T Off-Line Signature Recognition Based On Angle Features and GRNN Neural Networks
	%U https://publications.waset.org/pdf/17107
	%V 82
	%X This research presents a handwritten signature recognition based on angle feature vector using Artificial Neural Network (ANN). Each signature image will be represented by an Angle vector. The feature vector will constitute the input to the ANN. The collection of signature images will be divided into two sets. One set will be used for training the ANN in a supervised fashion. The other set which is never seen by the ANN will be used for testing. After training, the ANN will be tested for recognition of the signature. When the signature is classified correctly, it is considered correct recognition otherwise it is a failure.

	%P 1307 - 1311