Comparative Testing of 2D Stroke Gesture Recognizers in Multiple Contexts

The wide range of algorithms for recognizing two-dimensional stroke gestures raises a crucial question: which recognizer should be selected depending on the gesture set and its context of use? Since the context of use expresses a category of users performing interactive tasks on a device in a physical environment, many contextual conditions are feasible. Recognizers have been tested in only a few of them, making them difficult to compare and select. A recognizer suitable for one context of use does not necessarily translate to another. Recognizer variables, such as the number of resampling points, the number of templates, and the number of users, vary from one recognizer to another, making them challenging to specify for practitioners and heterogeneous to compare for researchers. To address these issues, this article performs comparative tests of 16 recognizers (15 state-of-the-art recognizers and one for rotation invariance) in 11 gesture sets (9 reference sets and 2 for multi-device rotation invariance) against their accuracy, speed, and algorithmic complexity, in user-(in)dependent scenarios based on the variables mentioned above. In particular, the impact of two contextual dimensions, i.e., the visual impairments of end users and input devices, on accuracy is further studied. The comparative testing results in an empirically validated decision table for the researcher to select a recognizer depending on contextual conditions and a flow chart for the practitioner to be guided. We break down our design implications into two categories: gesture acquisition, recognition, and gesture set design. We also publicly release Gester , a JavaScript software for conducting comparative testing of recognizers under various contextual conditions, allowing the practitioner to test a recognizer in a project and the researcher to benchmark recognizers.