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 extra...

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Bibliographic Details
Main Authors: Zaafouri, A., Sayadi, M., Fnaiech, F.
Format: Conference Proceeding
Language:English
Subjects:
Arabic characters
Character recognition
Feature extraction
Gabor filters
Handwriting recognition
local energy
Neural network classifier
Neural networks
off-line character recognition
Text recognition
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Summary: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.
DOI:10.1109/CCCA.2012.6417862