ABSTRACT

In this work, an attempt is made to extract minimum number of features to represent the pattern used as inputs for Feed Forward Back Propagation Neural Network (FFBPNN). The binary image of a pattern stored in the frame is partitioned into square regions. A feature from each region is computed by the density and co-ordinate distance of 1s.pixels. The neural network is trained with the extracted features and Root Mean Square Error (RMSE) obtained in the training process is used as performance indicator to stop the FFBPNN learning. Tested the proposed feature extraction and classification algorithms on the handwritten numeral database and found very good classification recognition rate.