Title: Multilingual Sign Language Recognition And Translation System For Enhanced Communication Among Deaf Persons In Nigeria.
Authors: Murtala Muhammad Chafe, Abubakar Balarabe
Volume: 10
Issue: 7
Pages: 272-275
Publication Date: 2026/07/28
Abstract:
This paper presents the design and development of an "AI-Driven Real-Time Sign Language Translation System for Communication Between Deaf Individuals and Non-Sign Language Users in Nigeria". Communication barriers between deaf individuals and people who do not understand sign language continue to create significant challenges in education, healthcare, workplaces, and social interactions. Existing sign language translation systems largely focus on international languages and provide limited support for indigenous Nigerian languages, thereby limiting their usefulness in local communities. The primary aim of this study is to design and develop an artificial intelligence-based multilingual sign language translation system capable of translating sign language gestures into three major Nigerian languages: Hausa, Igbo, and Yoruba. The study specifically seeks to develop an AI model mobile app for sign language recognition. The study adopted a design and development research methodology using an Agile software development approach. Data was collected from both primary and secondary sources, including sign language datasets, video recordings, research publications, and online resources. The collected data was preprocessed and used to train a hybrid deep learning model based on Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) techniques. The developed system captures sign language gestures through a camera, recognizes gesture patterns, and translates them into Hausa, Igbo, and Yoruba in real time. The results of the study demonstrated that the proposed system effectively recognized sign language gestures and generated corresponding multilingual translations. Performance evaluation indicated satisfactory accuracy, usability, and response time, suggesting that the system can significantly reduce communication barriers between deaf individuals and non-sign-language users. The proposed system contributes to assistive technology development by integrating artificial intelligence with indigenous language translation, thereby promoting accessibility, social inclusion, and improved communication opportunities for hearing-impaired individuals within Nigerian communities.