Adaptive local subspace classifier in on-line recognition of handwritten characters

Subsystems for on-line recognition of handwriting are needed in personal digital assistants (PDAs) and other portable handheld devices. We have developed a recognition system which enhances its accuracy by applying continuous adaptation to the user’s writing style. The forms of adaptation we have experimented with take place simultaneously with the normal operation of the system and, therefore, there is no need for separate training period of the device. The present implementation uses Dynamic Time Warping (DTW) in matching the input characters with the stored prototypes. The DTW algorithm implemented with Dynamic Programming (DP) is, however, both time and memory consuming. In our current research, we have experimented with methods that transform the elastic templates to pixel images which can then be recognized by using statistical or neural classification. The particular neural classifier we have used is the Local Subspace Classifier (LSC) of which we have developed an adaptive version.