Features for mode detection in natural online pen input

When the user is free to write anything, like handwriting, drawings, or gestures, techniques are required to distinguish between modes. Mode detection, preceding recognition, can be an important aid in applications that invite natural pen input. In this paper, a large amount of data, acquired in different contexts, is used to assess eight features on their suitability for mode detection. Six global features: length, area, compactness, eccentricity, circular variance, and closure, and two structural features: curvature, and perpendicularity, have shown to be particularly useful for determining whether a pen trajectory contains handwriting, lines, arrows, or geometric shapes. Using these eight features an overall performance on unseen data was achieved of 98.7%, using a KNN classifier. According to the principal components analysis of the data, the most important features were closure, curvature, perpendicularity, and eccentricity. The results of this study are employed in two large research projects on natural multi-modal interaction that pursue design, route map, and map annotation scenarios.