  Handwritten character recognition is an active area of research with
applications in numerous fields. Past and recent works in this field have
concentrated on various languages. Arabic is one language where the scope of
research is still widespread, with it being one of the most popular languages
in the world and being syntactically different from other major languages. Das
et al. \cite{DBLP:journals/corr/abs-1003-1891} has pioneered the research for
handwritten digit recognition in Arabic. In this paper, we propose a novel
algorithm based on deep learning neural networks using appropriate activation
function and regularization layer, which shows significantly improved accuracy
compared to the existing Arabic numeral recognition methods. The proposed model
gives 97.4 percent accuracy, which is the recorded highest accuracy of the
dataset used in the experiment. We also propose a modification of the method
described in \cite{DBLP:journals/corr/abs-1003-1891}, where our method scores
identical accuracy as that of \cite{DBLP:journals/corr/abs-1003-1891}, with the
value of 93.8 percent.
