Dynamic time warping for the recognition of single-stroke input

Michael Schmidt, Gerhard Weber

Abstract: The work at hand presents thorough investigations of dynamic time warping (DTW) for on-line recognition of single-stroke input. We survey and give a systematization of nearest neighbor methods and show how concepts of DTW outperform current approaches without adding too much complexity. Observing mostly conservative and ‘expensive’ utilization and underestimation of DTW in this field, the contribution of this work is to give best practices for enhancing template matching by application of DTW which can be implemented in few lines of code. Our survey is substantiated by tests regarding questions of proper feature selection, pre-processing, and suitable parametrization. To provide flexible, device-independent recognition and as human input is also affected by practice or exhaustion, premise is a classification without interference by an input’s natural variances in speed, translation, scaling, and rotation. A set of geometric features prevalent in literature is given and extended by own contributions. Various specifications for DTW are evaluated with three different test sets. Our results show that features based on distances and common step patterns are outperformed by specific types of chain codes and local constraints. Classification also benefits from global warping windows usually applied in the context of speech recognition.