  Handwritten character recognition has been the center of research and a
benchmark problem in the sector of pattern recognition and artificial
intelligence, and it continues to be a challenging research topic. Due to its
enormous application many works have been done in this field focusing on
different languages. Arabic, being a diversified language has a huge scope of
research with potential challenges. A convolutional neural network model for
recognizing handwritten numerals in Arabic language is proposed in this paper,
where the dataset is subject to various augmentation in order to add robustness
needed for deep learning approach. The proposed method is empowered by the
presence of dropout regularization to do away with the problem of data
overfitting. Moreover, suitable change is introduced in activation function to
overcome the problem of vanishing gradient. With these modifications, the
proposed system achieves an accuracy of 99.4\% which performs better than every
previous work on the dataset.
