An intelligent feature analyzer for handwritten character recognition

This paper is concerned with the development of an efficient feature analyzer for handwritten character recognition. Feature analyzer presented in this paper can reduce the large domain of feature space and extract invariable information. Feature extraction has been viewed from multi dimensional perspective. To cope with the fuzziness of the recognition problem, a nonlinear classifier based on back propagation algorithm was used for classification. Generalizing capability of the system was increased by using ensemble of neural networks instead of using regular neural network. Training and testing using 10 fold cross validation and resultant impressive recognition accuracy (More than 90%) proves the effectiveness of the scheme.