Two-dimensional Stroke Gesture Recognition
Nathan Magrofuoco, Paolo Roselli, Jean Vanderdonckt
Abstract
The expansion of touch-sensitive technologies, ranging from smartwatches to wall screens, triggered a wider use of gesture-based user interfaces and encouraged researchers to invent recognizers that are fast and accurate for end-users while being simple enough for practitioners. Since the pioneering work on two-dimensional (2D) stroke gesture recognition based on feature extraction and classification, numerous approaches and techniques have been introduced to classify uni- and multi-stroke gestures, satisfying various properties of articulation-, rotation-, scale-, and translation-invariance. As the domain abounds in different recognizers, it becomes difficult for the practitioner to choose the right recognizer, depending on the application and for the researcher to understand the state-of-the-art. To address these needs, a targeted literature review identified 16 significant 2D stroke gesture recognizers that were submitted to a descriptive analysis discussing their algorithm, performance, and properties, and a comparative analysis discussing their similarities and differences. Finally, some opportunities for expanding 2D stroke gesture recognition are drawn from these analyses.