From Signs to Words and Speech: Multilingual Sign Language Conversion

Abstract

Good communication holds immense importance when we talk about the progress of society. However, not everyone is fortunate enough to communicate with each other directly. Some people use sign language as their only mode of communication but, unfortunately, sign language is not a universally understood language. We provide a novel approach—a deep learning model for sign language to text conversion—aimed at addressing the complexity of sign language variation. This technology combines computer vision and deep learning algorithms and recognizes hand movements via webcam while translating them into equivalent text. Furthermore, by adapting a multilingual strategy, we aim to reduce the barriers that are often faced by hearing impaired individuals. Our goal is to transform communication for the deaf by removing barriers that prevent them from expressing their ideas and views and promote a society where hearing impaired individuals would feel less alienated compared to the rest of world. By combining technology and empathy, this project will promote inclusiveness by creating such a world where every individual’s voice will be heard and respected, regardless of any disabilities.