Performance Testing of Stroke Gesture Recognizers with Gester

The performance of state-of-the-art stroke gesture recognizers has been extensively tested against accuracy and speed in the user-dependent or user-independent procedure on a wide variety of gesture sets. These tests were carried out under heterogeneous experimental conditions, making the results non-comparable from one source to another, and calling for a consistent assessment that ensures reproducibility of the results. When a new gesture set should be incorporated in a gesture-based user interface and/or when a new recognizer is made available, this gesture set needs to be tested with existing and new recognizers to determine which recognizer offers the best performance in a given context of use. To this end, this technical note presents Gester, a software that automates the performance testing of stroke gesture recognizers in terms of accuracy (through three recognition rates) and speed (through four execution times). It exports data for visualizing confusion matrices and confusion wheels. We elaborate on typical use cases supported by Gester, which include the two traditional testing procedures, i.e., user-dependent and user-independent scenarios, and provide two new ones, i.e., dataset-dependent and dataset-independent, to test the performance of recognizers under new conditions. Then, we illustrate a series of activities.