Stanley Leonard, Joseph Shagina
Hearing impairment is a growing global health challenge that disproportionately affects people in resource-constrained countries such as Tanzania, where access to assistive technologies remains limited. Conventional solutions, including hearing aids, cochlear implants, and interpreters, are often costly and inaccessible. This study proposes an AI-driven real-time Swahili Sign Language Recognition system designed to bridge communication gaps between the hearing-impaired community and the general population. The system integrates Convolutional Neural Networks (CNNs), Scale-Invariant Feature Transform (SIFT), and Long Short-Term Memory (LSTM) with computer vision techniques to recognize and interpret sign gestures. SIFT extracts key features from images captured via webcam, while CNN performs classification and LSTM models temporal dependencies for improved accuracy. Experimental results reveals high recognition accuracy of 98.5% and a low error rate of 0.1345%, outperforming several existing models. The system offers an effective, affordable solution for enhancing communication among Swahili-speaking communities.