Online handwritten gesture recognition based on Takagi-Sugeno fuzzy models

In this paper, we present a new method for incremental online handwritten gesture recognition based on fuzzy rules. This approach allows starting from a scratch with no previously learned classes and adding new ones lifelong. Unlike methods based on evolving mountain clustering, our approach suits incremental concept better. We introduce a new method for evolving clustering and usage of incremental density measurement for determining the membership function which significantly improves the results. Density measurement as membership function allows using only few parameters instead of the costly covariance matrices and does not require any estimating by averaging and thus preventing from information lost. We also introduce a new set of features based on a shape of gestures. Combination of these new system characteristics thus lowers memory and computational requirements while significantly increasing recognition rate.