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An Empirical Evaluation of Off-line Arabic Handwriting And Printed Characters Recognition System

Keywords: Recognition , Hidden Markov Models (HMMs) , Arabic Word , Feature Extraction , off-line handwriting. , IJCSI

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Handwriting recognition is a challenging task for many real-world applications such as document authentication, form processing, historical documents. This paper focuses on the comparative study on off-line handwriting recognition system and Printed Characters by taking Arabic handwriting. The off-line Handwriting Recognition methods for Arabic words which being often used among then across the Middle East and North Africa people. In this paper we are proposing off-line Arabic handwriting and printed characters and the language used by the majority of the Middle East. We are using discrete Hidden Markov Models (HMM) for Arabic handwriting and printed characters for the final recognition. In this paper after preprocessing step the characters are auto-segmented using a recursive algorithm as sequences of connected neighbors along lines and curves and Arabic words are first pre-classified into one of known character groups, based on the structural properties of the text line. The proposed system was trained and tested Arabic character images. The Arabic characters were written by the different people on a preformatted paper and the method recognizes the Arabic handwriting in print style format. A comparative Experimental result has shown 93.40% recognition rate for the Arabic handwriting and 97.30% recognition rate for the Arabic printed characters.


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