Handwritten character recognition is an active area of research withapplications in numerous fields. Past and recent works in this field haveconcentrated on various languages. Arabic is one language where the scope ofresearch is still widespread, with it being one of the most popular languagesin the world and being syntactically different from other major languages. Daset al. \cite{DBLP:journals/corr/abs-1003-1891} has pioneered the research forhandwritten digit recognition in Arabic. In this paper, we propose a novelalgorithm based on deep learning neural networks using appropriate activationfunction and regularization layer, which shows significantly improved accuracycompared to the existing Arabic numeral recognition methods. The proposed modelgives 97.4 percent accuracy, which is the recorded highest accuracy of thedataset used in the experiment. We also propose a modification of the methoddescribed in \cite{DBLP:journals/corr/abs-1003-1891}, where our method scoresidentical accuracy as that of \cite{DBLP:journals/corr/abs-1003-1891}, with thevalue of 93.8 percent.
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