Abstract
The field of Arabic handwriting recognition and translation is currently experiencing rapid growth in terms of research, which is evident in the coverage of major conferences and journals that specialise in the area of handwriting recognition. Against this backdrop, a significant increase has been observed in the classification and features techniques used, as compared to some years back. Researchers have put in more efforts geared towards building a variety of databases for Arabic handwriting recognition. This article aims to provide a comprehensive survey of advances in Arabic offline handwriting recognition. We have been provided details of availability Arabic databases with limitation. Further, we focus on techniques of feature extraction and different variety of classification approaches such as ANN, HMM, SVM that used in Arabic handwriting recognition.
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Acknowledgements
The author would like to acknowledge Universiti Tun Hussein Onn Malaysia (UTHM) for the support of this research under the Tier 1 Grant: vot H093.
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Ghadhban, H.Q., Othman, M., Samsudin, N.A., Ismail, M.N.B., Hammoodi, M.R. (2020). Survey of Offline Arabic Handwriting Word Recognition. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_34
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DOI: https://doi.org/10.1007/978-3-030-36056-6_34
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