DG3: Exploiting Gesture Declarative Models for Sample Generation and Online Recognition

In this paper, we introduce DG3, an end-to-end method for exploiting gesture interaction in user interfaces. The method allows to declaratively model stroke gestures and their sub-parts, generating the training samples for the recognition algorithm. In addition, we extend the algorithms of the $-family for supporting the online (i.e., real-time ) stroke recognition and their parts, as declared in the models. Finally, we show that the method outperforms existing approaches for online recognition and has comparable accuracy with offline methods after a few gesture segments.