Browsing by Author "Jomah,O.S.M."

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    A Comparison of Pattern Recognition Approaches for Recognizing Handwriting in Arabic Letters
    (Institute of Electrical and Electronics Engineers Inc., 2021) Douma,A.; Ahmed,A.A.; Sengul,G.; Santhosh,J.; Jomah,O.S.M.; Ibrahim Salem,F.G.
    For Arabic letters recognition, we achieve three of pattern recognition approaches namely gray level co-occurrence matrix (GLCM), local binary pattern recognition (LBP) and artificial neural network (ANN) and compare between them to result best performance. Two of these methods level co-occurrence matrix and local binary pattern recognition are used for feature extraction whereas in artificial neural network (ANN) we use the intensity values of pixels for input of the neural network. Two classifiers are used, the K-Nearest Neighbor classifier (KNN) for the LBP, GLCM and neural network classifier for (ANN) artificial neural network. Also, we evaluate the results by using leave one person out approach, fold classification and leave one out. © 2021 IEEE.