EdgeNet: A novel approach for Arabic numeral classification
This work addresses the need for robust classification of handwritten Arabic numerals, which is important for applications in digitization and recognition, but it is incremental as it builds on existing deep learning approaches with specific enhancements.
The authors tackled the problem of handwritten Arabic numeral classification by merging and augmenting existing datasets to increase data diversity and proposing a novel deep model that uses low-level edge features with residual connections. Their model achieved a validation accuracy of 99.59%, outperforming existing state-of-the-art methods.
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.