A new composite feature vector for Arabic handwritten signature recognition

One of the main difficulties in solving complex recognition problems is to find an optimum feature vector that translates the input image to a set of numeric values to be presented to the classifier. Optimum in the sense that it classifies samples correctly, it is easy to compute and it is small in size. This step is essential to reduce the amount of data presented to the classifier. Even if we have an excellent learning classifier, the role of the feature vector should not be underestimated. We present a comparative study for a large number of features (210) previously studied in the literature as applied to the problem of recognizing Arabic handwritten signatures. Based on the statistical results of this study, a new feature vector was suggested and tested. It yielded 98.6% recognition rate for our specific application. Since signature were represented as a 2D array of binary values output from a scanner, it is our view that the proposed vector can be generalized to the problem of recognizing any limited number of 2D binary patterns