%0 Journal Article %1 Willems2009 %A Willems, Don %A Niels, Ralph %A van Gerven, Marcel %A Vuurpijl, Louis %D 2009 %J Pattern Recognition %K Iconic features gestures pattern recognition %P - %R DOI: 10.1016/j.patcog.2009.01.030 %T Iconic and multi-stroke gesture recognition %U http://www.sciencedirect.com/science/article/B6V14-4VJM2R7-1/2/173a5c3e0e9de6343d66ac119cf9ccb1 %V In Press, Corrected Proof %X 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.

@article{Willems2009, abstract = {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.}, added-at = {2009-04-22T12:06:00.000+0200}, author = {Willems, Don and Niels, Ralph and van Gerven, Marcel and Vuurpijl, Louis}, biburl = {https://www.bibsonomy.org/bibtex/2571ea0e2bb05887f4da30e65f2f6f86e/dieudonnew}, description = {ScienceDirect - Pattern Recognition : Iconic and multi-stroke gesture recognition}, doi = {DOI: 10.1016/j.patcog.2009.01.030}, interhash = {56fe1f810e64f1406338769cdd404489}, intrahash = {571ea0e2bb05887f4da30e65f2f6f86e}, issn = {0031-3203}, journal = {Pattern Recognition}, keywords = {Iconic features gestures pattern recognition}, pages = { - }, timestamp = {2009-04-22T12:06:01.000+0200}, title = {Iconic and multi-stroke gesture recognition}, url = {http://www.sciencedirect.com/science/article/B6V14-4VJM2R7-1/2/173a5c3e0e9de6343d66ac119cf9ccb1}, volume = {In Press, Corrected Proof}, year = 2009 }