Real-Time American Sign Language Translation Using Deep Learning

Sign language is a means of communication between the deaf and hard of hearing, but sometimes becomes difficult to communicate with others who do not know sign language. Traditional methods of translation usually result in imprecise conversions and sometimes duplication. This paper is interested in the design and implementation of a novel system for the conversion of sign language to spoken English. The system applies computer vision and machine learning techniques toward better interpretation of the motion in sign language and produces natural human language output. A comprehensive discussion of translation methods that exist and why they fail in the task is provided. It underlines how authentic voice translations, that are contextually accurate, have to be generated to enhance the communicative inclusion. This technique integrates deep learning models that have been trained on large sign language datasets to achieve great accuracy in detecting and interpreting sign language movements. It tries to get high accuracy in identifying and understanding sign language motions, in order to avoid mistakes and assure smooth translation. The system will be tested extensively under various sign language inputs after which the accuracy of given textual translations will be known. The research outcome creates value in the development of inclusive communication technology because it would bring more light on issues revolving around authenticity in the interpretation process of sign language translation. An approach to this contribution would improve the quality and quality of sign language interpretation; it would promote effective communication, understanding, and appreciation amongst linguistic groups.