A CNN-Based Arabic Diacritic Symbol Recognition System Using Domain Adaptation

Abstract

The recognition of Arabic diacritic symbols is essential for accurate comprehension of Arabic texts, yet most existing Optical Character Recognition (OCR) systems lack this capability. This paper proposes a novel approach for Arabic diacritic recognition using a domain adaptation method applied to the AlexNet deep convolutional neural network architecture. A custom dataset of synthetic Arabic diacritic images was generated, incorporating variations in font styles, sizes, slant angles, and perspective distortions. The model was trained using this dataset, and a domain adaptation technique was employed to improve its generalization across diverse diacritic styles and handwriting variations. We evaluated the proposed model on the testing dataset consisting of a variety of fonts and distortions; the result was an overall accuracy of 97%, indicating that the model can achieve a high accuracy for most diacritic categories, with only a few misclassifications.