This paper presents a novel system approach for online Arabic handwriting recognition. The approach segments the word using new character boundaries detection based algorithm. Moreover it employs HMM-based classification method for the recognition. A dataset: Sudan University of Science and Technology Online Arabic Handwriting (SUSTOLAH) is used in testing the proposed approach. Promising experimental results of testing the approach with the dataset are provided
Recognition of Arabic handwriting has attracted the interest of researchers for many years. Until now it has been a challenging research area due to many issues. The feature extraction is an essential stage in the recognition systems of handwriting. The main idea behind this paper is to study EDMs (Edge Direction Matrixes) as a feature extraction technique for Online Arabic Handwriting Recognition. In this study, SUSTOLAH datasets will be used, in which datasets of online Arabic handwriting are presented in Sudan University of Science and Technology. In this paper, satisfactory results have been achieved, where the value of the correlation/regress coefficient for the differences between the variant handwritten characters is found to be -0.01322.
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