!FTL, an Articulation-Invariant Stroke Gesture Recognizer with Controllable Position, Scale, and Rotation Invariances

Nearest neighbor classifiers recognize stroke gestures by computing a (dis)similarity between a candidate gesture and a training set based on points, which may require normalization, resampling, and rotation to a reference before processing. To eliminate this expensive preprocessing, this paper introduces a vector-between-vectors recognition where a gesture is defined by a vector based on geometric algebra and performs recognition by computing a novel Local Shape Distance (LSD) between vectors. We mathematically prove the LSD position, scale, and rotation invariance, thus eliminating the preprocessing. To demonstrate the viability of this approach, we instantiate LSD for n=2 to compare !FTL, a 2D stroke-gesture recognizer with respect to $1 and $P, two state-of-the-art gesture recognizers, on a gesture set typically used for benchmarking. !FTL benefits from a recognition rate similar to $P, but a significant smaller execution time and a lower algorithmic complexity.