A trainable gesture recognizer

摘要

Gestures are hand-drawn strokes that do things. These things happen at distinctive places on the stroke. We built a gesture input filter and recognizer. The input filter is fast, because it does few computations per input point, because it can omit pre-filter data smoothing, and because wild points caused by hardware glitches are removed at the few output points of the filter, not at the many input points. The recognizer is a novel combination of two traditional techniques; angle filtering and multiscale recognition. Because an angle filter does not produce well-behaved scaled output, the multi-scale treatment had to be unusual.