Effect of Intrinsic Parameters on Efficiency of Gradient Features
In this paper, the effect of intrinsic parameters is investigated on the efficiency of the robust and state of the art Gradient Features used for handwriting recognition. Ways are found to boost the recognition accuracy of these features. The Gradient Feature parameters investigated, include number of planes, number of components and gradient operator used. The classification accuracy of handwritten digits using modified Gradient Features improves when the new parameters are used. Nearest Neighbor (k-NN) and Support Vector Machine (SVM) algorithms have been used for classification purposes. The data sets used are the Semeion Dataset and USPS Dataset.
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