Deep learning-based eye sign communication system for people with speech impairments

<b>Methods</b>: In this research, we propose an eye sign-based communication system that operates on deep learning principles and accepts eye sign patterns from speech-impaired or paraplegic individuals via a standard webcam. The system converts the eye signs into alphabets, words, or sentences and displays the resulting text visually on the screen. In addition, it provides a vocal prompt for the user and the caretaker. It functions effectively in various lighting conditions without requiring calibration and integrates a text prediction function for user convenience. Impact Experiments conducted with participants aged between 18 and 35 years yielded average accuracy rates of 98%, 99%, and 99% for alphabet, word, and sentence formation, respectively. These results demonstrate the system's robustness and potential to significantly benefit individuals with speech impairments.