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In this paper an ensemble model is proposed for the recognition of Odia handwritten character. The ensemble model is constructed from four base classifiers: Support Vector Machine (SVM), Artificial Neural Network (ANN), C5.0 Decision Tree and Discriminant Analysis (DA). Gradient and curvature based features are extracted from the numerals and a combination of gradient and curvature based features...
The paper develops an efficient but simple adaptive nonlinear classifier for recognition of handwritten Odiya numerals. The standard gradient and curvature features are extracted and nonlinearly mapped by sine/cosine expansions. These nonlinear inputs are fed to a low complexity classifier. The simulation results show excellent classification accuracy when test features are used.
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