Sketched symbol recognition using Zernike moments

Abstract

We present an on-line recognition method for hand-sketched symbols. The method is independent of stroke-order, -number, and -direction, as well as invariant to scaling, translation, rotation and reflection of symbols. Zernike moment descriptors are used to represent symbols and three different classification techniques are compared: support vector machines (SVM), minimum mean distance (MMD), and nearest neighbor (NN). We have obtained a 97% recognition accuracy rate on a dataset consisting of 7,410 sketched symbols using Zernike moment features and a SVM classifier.