Development of a System for Translating Frequently Used Turkish Sign Language Words into Text for the Hearing Impaired

Communication involves the exchange of emotions, thoughts, information, and news among individuals and takes various forms, encompassing both verbal and non-verbal methods. Sign language, utilized by individuals who are deaf or hard of hearing, relies on gestures and facial expressions. Sign language is not a universal system; instead, it varies significantly across different countries, with each nation having its own distinct version. Each sign comprises three main components: hand shape, hand position, and hand movement. This study aims to develop a system that recognizes the most commonly used words in Turkish Sign Language (TSL) and converts these signs into text. The system utilizes an image processing algorithm to detect and translate these words, facilitating effective communication for individuals who are Deaf or Hard of Hearing. The dataset includes 20 frequently used words, collected from 12 individuals, and trained using the YOLOv8 machine learning algorithm. The model achieved an accuracy rate of 99.4%, demonstrating its effectiveness in real-world conditions. This system aims to improve the daily interactions and communication experiences of Deaf or Hard of Hearing individuals by providing a reliable tool for sign language translation.