Applying Data Augmentation to Handwritten Arabic Numeral Recognition Using Deep Learning Neural Networks
This work addresses the problem of recognizing handwritten Arabic numerals, which is important for applications in pattern recognition and AI, but it is incremental as it builds on existing deep learning methods with specific tweaks.
The paper tackled handwritten Arabic numeral recognition by proposing a convolutional neural network model with data augmentation, dropout regularization, and activation function modifications, achieving 99.4% accuracy, which outperforms previous works on the dataset.
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.