Detection of Hijaiyah Letters Handwritten in Early Childhood Using Yolov8

Author's Country: Indonesia

Abstract

This study investigates the effectiveness of the YOLOv8 (You Only Look Once version 8) algorithm in detecting handwritten Hijaiyah letters among early childhood learners. The introduction of technology in early childhood education is essential for enhancing literacy skills, particularly in learning the Arabic alphabet, which is crucial for reading the Quran. This research addresses the challenges faced by educators in assessing children's handwriting, which often lacks consistency and objectivity. A dataset of 3,780 images of handwritten Hijaiyah letters was collected from children at RA BAIPAS Roudlotul Jannah, including various writing styles to ensure the model's robustness. Prior to training, the images underwent preprocessing steps such as resizing, normalization, and data augmentation techniques like rotation and flipping to enhance the quality and diversity of the training data. The YOLOv8 model was trained using an 80-10-10 split for training, validation, and testing datasets. Evaluation metrics such as precision, recall, and mean Average Precision (mAP) were used. The results showed that YOLOv8 achieved an impressive accuracy of 96.08% in detecting handwritten Hijaiyah letters, with high precision and recall rates further validating the model's reliability. This research highlights the potential of integrating advanced object detection algorithms like YOLOv8 into educational practices. By providing real-time feedback, the system can significantly enhance the learning experience for young children, facilitating their understanding and mastery of the Arabic alphabet. Future research should focus on expanding the dataset and refining the model to address handwriting variability challenges and improve accuracy.