Abstract
When the user is free to write or draw anything with a pen, tech\-niques are required to distinguish between the different modes of pen input. 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.
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