Development of A Computer Aided Real-Time Interpretation System for Indigenous Sign Language in Nigeria Using Convolutional Neural Network

Sign language is the primary method of communication adopted by deaf and hearing-impaired individuals. The indigenous sign in Nigeria one area receiving growing interest, with major challenge faced between signers non-signers. Recent advancements computer vision deep learning neural networks (DLNN) have led to exploration necessary technological concepts towards tackling existing challenges. One extensive impact from use DLNN interpretation hand signs. This study presents an system for Nigeria. methodology comprises three key phases: dataset creation, techniques, model development. A multi-class Convolutional Neural Network (CNN) designed train interpret signs evaluated using a custom-built some selected words comprising 15000 image samples. experimental outcome shows excellent performance system, accuracy attaining 95.67%.