  Despite the importance of handwritten numeral classification, a robust and
effective method for a widely used language like Arabic is still due. This
study focuses to overcome two major limitations of existing works: data
diversity and effective learning method. Hence, the existing Arabic numeral
datasets have been merged into a single dataset and augmented to introduce data
diversity. Moreover, a novel deep model has been proposed to exploit diverse
data samples of unified dataset. The proposed deep model utilizes the low-level
edge features by propagating them through residual connection. To make a fair
comparison with the proposed model, the existing works have been studied under
the unified dataset. The comparison experiments illustrate that the unified
dataset accelerates the performance of the existing works. Moreover, the
proposed model outperforms the existing state-of-the-art Arabic handwritten
numeral classification methods and obtain an accuracy of 99.59% in the
validation phase. Apart from that, different state-of-the-art classification
models have studied with the same dataset to reveal their feasibility for the
Arabic numeral classification. Code available at
http://github.com/sharif-apu/EdgeNet.
