Static Handwritten Signature Recognition Using Discrete Random Transform and Combined Projection Based Technique

In this paper, we proposed Discrete Radon Transform (DRT) technique for feature extraction of static signature recognition to identify forgeries. Median filter has been introduced for noise cancellation of handwritten signature. This paper describes static signature verification techniques where signature samples of each person was collected and cropped by automatic cropping system. Projection based global features are extracted like Horizontal, Vertical and combination of both the projections, these all are one dimensional feature vectors to recognize the handwritten static signature. The distance between two corresponding vectors can be measured with Dynamic Time Warping algorithm (DTW) and using only six genuine signatures samples of each person has been employed here in order to train our system. In the proposed system process time required for training our system for each person is between 1.5 to 4.2 seconds and requires less memory for storage. The optimal performance of the system was found using proposed technique for Combined projection features and it gives FAR of 5.60%, FRR of 8.49% and EER 7.60%, which illustrates such new approach to be quite effective and reliable.