Dynamic character recognition using an elastic matching
Numerous methods based on distance computation are available to carry out vectors 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 centroïd computation algorithm is proposed. Finally, the learning scheme algorithm has been successfully applied on an on-line numerical handwritten character recognition problem using a previously computed centroïd of a set of prototypes.
KeywordsHandwritten character recognition clustering elastic matching
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