Toward affine recognition of handwritten mathematical characters
DAS '10 Proceedings of the 9th IAPR International Workshop on Document Analysis Systems
Polynomial approximation in handwriting recognition
Proceedings of the 2011 International Workshop on Symbolic-Numeric Computation
A streaming digital ink framework for multi-party collaboration
CICM'12 Proceedings of the 11th international conference on Intelligent Computer Mathematics
CICM'12 Proceedings of the 11th international conference on Intelligent Computer Mathematics
Determining points on handwritten mathematical symbols
CICM'13 Proceedings of the 2013 international conference on Intelligent Computer Mathematics
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We study online classification of isolated handwritten symbols using distance measures on spaces of curves. We compare three distance-based measures on a vector space representation of curves to elastic matching and ensembles of SVM. We consider the Euclidean and Manhattan distances and the distance to the convex hull of nearest neighbors. We show experimentally that of all these methods the distance to the convex hull of nearest neighbors yields the best classification accuracy of about 97.5%. Any of the above distance measures can be used to find the nearest neighbors and prune totally irrelevant classes, but the Manhattan distance is preferable for this because it admits a very efficient implementation. We use the first few Legendre-Sobolev coefficients of the coordinate functions to represent the symbol curves in a finite-dimensional vector space and choose the optimal dimension and number of bits per coefficient by cross-validation. We discuss an implementation of the proposed classification scheme that will allow classification of a sample among hundreds of classes in a setting with strict time and storage limitations.