Efficient recognition of Odiya numerals using low complexity neural classifier

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.