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17th International Conference on Pattern Recognition (ICPR'04) - Volume 1
Sketched Symbol Recognition using Zernike Moments
Cambridge UK
August 23-August 26
ISBN: 0-7695-2128-2
Heloise Hse, University of California at Berkeley
A. Richard Newton, University of California at Berkeley
In this paper, we present an on-line recognition method for hand-sketched symbols. The method is independent of stroke-order, -number, and -direction, as well as invariant to scaling, translation, rotation and reflection of symbols. Zernike moment descriptors are used to represent symbols and three different classification techniques are compared: Support Vector Machines (SVM), Minimum Mean Distance (MMD), and Nearest Neighbor (NN). We have obtained a 97% recognition accuracy rate on a dataset consisting of 7,410 sketched symbols using Zernike moment features and a SVM classifier.
Citation:
Heloise Hse, A. Richard Newton, "Sketched Symbol Recognition using Zernike Moments," icpr, vol. 1, pp.367-370, 17th International Conference on Pattern Recognition (ICPR'04) - Volume 1, 2004
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