Iconic and multi-stroke gesture recognition
until further notice
SourcePattern Recognition, 42, 12, (2009), pp. 3303-3312
Article / Letter to editor
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SW OZ DCC AI
SW OZ NICI KI
SubjectCognitive artificial intelligence; DI-BCB_DCC_Theme 4: Brain Networks and Neuronal Communication
Many handwritten gestures, characters, and symbols comprise multiple pendown strokes separated by penup strokes. In this paper, a large number of features known from the literature are explored for the recognition of such multi-stroke gestures. Features are computed from a global gesture shape. From its constituent strokes, the mean and standard deviation of each feature are computed. We show that using these new stroke-based features, significant improvements in classification accuracy can be obtained between 10% and 50% compared to global feature representations. These results are consistent over four different databases, containing iconic pen gestures, handwritten symbols, and upper-case characters. Compared to two other multi-stroke recognition techniques, improvements between 25% and 39% are achieved, averaged over all four databases.
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