A new learning scheme for the recognition of dynamical handwritten characters

Vector comparison is essential in pattern recognition. Numerous methods based on distance computation are available to carry out such comparison. Unfortunately most of them are applicable only if the vectors are of the same length or do not take into account components misalignment. This paper presents a new distance between two representations called the elastic distance and based on the dynamic programming technique. Properties are studied. We show that it leads to a variant of the least vector quantisation technique that learns the best representants of a group of prototypes. A new centroid computation algorithm is proposed. Finally, the learning scheme algorithm has been successfully applied on an online numerical handwritten character recognition problem using a previously computed centroid of a set of prototypes.