Signature samples of each person was collected and taken as the input image. After thresholding the input image the signature was converted into binary format. Then for the binary image inversion was performed which changes the signature. Then the signature was cropped by automatic cropping system. By using median filter the signature image was filtered. or feature extraction discrete radon transform(DRT) technique was used for static signature recognition to identify forgeries. The performance are extracted in horizontal , vertical and combination of both projection to recognize the signature. 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 , FRR and EER which illustrates such new approach to be quite effective and reliable.
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