The achieved FAR is 5.3% and the FRR is 4.0. Ali Karouni, Bassam Daya, Samia Bahlak [3] introduce offline signature recognition using neural network approach. The geometric features extracted and classification using ANN. Get the threshold of 90%, FAR of 1.6% and FRR of 3% and the classification ratio is 93%. Vu Nguyen, Midheal Blumenstein Graham Leedham [4] proposed a global functionality for the offline signature verification problem. Global features based on a signature boundary and its projections. SVM classifier is used for better accuracy and classification. The first global characteristic comes from the total “energy” a writer uses to create the signature. The second presents intake information from horizontal and vertical projections of the signature. The FRR is 17.25% and the FAR for random and targeted counterfeits is 0.08 and