Printed Arabic character recognition using local energy and structural features

This paper presents a method of isolated Arabic character recognition using local energy and structural features. The method requires skeletonization in order to facilitate feature extraction process. The thinned character image is convolved with log Gabor filters bank for local energy feature extraction. Also, structural features such as: dots, endpoints and loops, are extracted from the skeleton to make easy the recognition stage. The characters are classified and recognized using multiplayer perceptron neural network MLP. Simulation results prove that the proposed set of features gives satisfactory recognition rate. Also the recognition system using local energy model demonstrates its rotation, scale and translation invariant.