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Zernike Moments and Neural Networks for Recognition of Isolated Arabic Characters
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2012
Year
Image AnalysisEngineeringOptical Character RecognitionArabicPattern RecognitionIsolated Arabic CharactersBiometricsZernike MomentsMultilayer Neural NetworksFeature ExtractionText RecognitionArabic OrthographyNeural NetworksStatistical Pattern RecognitionLanguage StudiesIsolated ArabicCharacter RecognitionDocument Processing
The aim of this work is to present a system for recognizing isolated Arabic printed characters. This system goes through several stages: preprocessing, feature extraction and classification. Zernike moments, invariant moments and Walsh transformation are used to calculate the features. The classification is based on multilayer neural networks. A recognition rate of 98% is achieved by using Zernike moments.