Alphabet Recognition in Arabic Sign Language: A Machine Learning Perspective

Abstract

Pattern recognition in human-computer interaction systems has gained significant attention in recent years, particularly in computer vision and machine learning applications. One prominent application is the recognition of hand gestures used in communication with deaf individuals, specifically in identifying the dashed letters within Quranic surahs. This paper proposes a new alphabet-based Arabic sign language recognition model, which employs a vision-based approach. The system comprises four stages: data acquisition, data preprocessing, feature extraction, and classification. The proposed model accommodates three types of datasets: bare hands against a dark background, bare hands against a light background, and hands wearing dark-colored gloves. The process begins with capturing an image of the alphabet gesture, followed by hand separation and background isolation. Hand features are then extracted based on the chosen method. In terms of classification, supervised learning techniques are employed to classify the 28-letter Arabic alphabet using 9,240 images. The focus is on classifying the 14 alphabetic letters representing the initial Quranic surahs in the Quranic sign language. The experimental results demonstrate that the new proposed model has achieved an impressive accuracy of 99.5% using the k nearest neighbor classifier.