Abstract
Arabic script is considered to be one of the most complex writing systems, which complicate the text recognition task. Among its complexities, the shape of the character depends according to its position in the word. More than 170 different shapes could be constructed to represent 28 basic letters; some of them are more used than others in the Arabic writing. To make training and recognition of characters more efficient, a study on shape modelling of different handwritten Arabic characters seems to be important. A segmentation-free word recognition system based on Hidden Markov Models (HMMs) is used to conduct this study. Experimental results are given for different sets of shape models using the IFN/ENIT database which contains an important number of handwritten Arabic words covering different writing styles.
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Acknowledgment
We would like to thank the REsearch Group on Intelligent Machines (REGIM) and the Institute for Communications Technology (IFN) in Braunschweig Technical University, specially Dr. Fouad Slimane, for supporting this research and providing the computing facilities.
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Mezghani, A., Kallel, F., Kanoun, S., Kherallah, M. (2018). Contribution on Character Modelling for Handwritten Arabic Text Recognition. In: Abraham, A., Haqiq, A., Ella Hassanien, A., Snasel, V., Alimi, A. (eds) Proceedings of the Third International Afro-European Conference for Industrial Advancement — AECIA 2016. AECIA 2016. Advances in Intelligent Systems and Computing, vol 565. Springer, Cham. https://doi.org/10.1007/978-3-319-60834-1_37
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DOI: https://doi.org/10.1007/978-3-319-60834-1_37
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