Controlling on-line adaptation of a prototype-based classifier for handwritten characters

Methods for controlling the adaptation process of an online handwritten character recognizer are studied. The classifier is based on the k-nearest neighbor rule and it is adapted to a new writing style by adding new prototypes, deactivating confusing prototypes, and reshaping existing prototypes in a self-supervised fashion. The dissimilarity measure used for the comparison of characters is a nonlinear curve matching method base on dynamic time warping algorithm. Time needed for the evaluation of the dissimilarity measure for a single character depends linearly on the size of the prototype set. The purpose of the control methods is to increase the classifier´s tolerance to malformed or mislabelled learning samples and to limit the growth of the prototype set. The control methods either set an upper limit for the number of prototypes per class or switch the adaptation of a particular character class on or off depending on the earlier performance of the classifier