Machine assessment of shape copying tests using Zernike moment descriptors
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Pattern Recognition Letters
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Pattern Recognition
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ICIAR'11 Proceedings of the 8th international conference on Image analysis and recognition - Volume Part I
Technical Section: Neural network-based symbol recognition using a few labeled samples
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MIWAI'11 Proceedings of the 5th international conference on Multi-Disciplinary Trends in Artificial Intelligence
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Pattern Recognition Letters
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Journal of Visual Communication and Image Representation
Spectra of shape contexts: An application to symbol recognition
Pattern Recognition
A new geometric descriptor for symbols with affine deformations
Pattern Recognition Letters
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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.