Core Points - Variable and Reduced Parameterization for Symbol Recognition
Recent research in the field of on-line handwriting recognition has been focused on statistical systems such as Hidden Markov Models, Neural Networks or a combination of these. There are however merits of employing an approach based on template matching. The first part of this thesis presents a new strategy for parameterization of on-line handwritten character samples. A novel efficient template m
