Gesture to Speech Transformation: A Real-Time Sign Language Alphabet Recognition System for Digital Inclusion of the Speech Impaired Community

Abstract

Access to communication is a fundamental right of every individual, including the speech-impaired community, which often faces limitations in social interaction. This study designs a real-time system that transforms sign language alphabet gestures into speech by utilizing Computer Vision and Deep Learning technologies. A sign language alphabet dataset is processed using a Convolutional Neural Network (CNN) to recognize visual hand patterns representing letters A–Z. The trained model is then integrated with OpenCV and Mediapipe for real-time hand gesture detection and connected to a speech synthesis engine so that the recognized letters can be automatically spoken. The results demonstrate the system’s potential as a basic communication bridge that supports digital inclusion for the speech-impaired community. From a sustainable development perspective, this innovation is relevant to SDG 4 (Quality Education) and SDG 10 (Reduced Inequalities), as it enables more equitable, inclusive, and sustainable social interaction in the era of digital transformation. This innovation can also serve as a foundation for further development toward an automatic sign language translator capable of recognizing full words and complete sentences. Consequently, this system has strong potential to become a practical solution for ensuring equal access to communication across various sectors, including education, public services, and the workplace.