Vector-based, Structure Preserving Stroke Gesture Recognition

Magrofuoco, Nathan [UCL] Roselli, Paolo [UCL] Perez Medina, Jorge Luis [UCL] Vanderdonckt, Jean [UCL] Villarreal Narvaez, Santiago [UCL]

Unistroke and multistroke gesture recognizers have always striven to reach some robustness with respect to all variations encountered when people issue gestures by hand on touch surfaces or with sensing devices. For this purpose, successful stroke recognizers rely on a gesture recognition algorithm that satisfies a series of invariance properties such as: stroke-order invariance, stroke-number invariance, stroke direction, invariance, position, scale, and rotation invariance. Before initiating any recognition activity, these algorithms ensure these properties by performing several pre-processing operations. These operations induce an additional computational cost to the recognition process, as well as a potential error bias. To cope with this problem, we introduce an algorithm that ensures all these properties analytically instead of statistically based on a vector algebra. Instead of points, the recognition algorithm works on vectors between vectors. We demonstrate that this approach not eliminates the need for these preprocessing operations but also satisfies an entire structure preserving transformation.