Greek sign language recognition using machine learning

Sign language involves gestures.Grammar and syntax of sign language is very complex to understand.Hence, sign language recognition (SLR) systems address this issue.Artificial intelligence (AI) and computer vision help develop SLR systems that automatically recognize and interpret sign language gestures and convert them into text or audio.Rulebased, machine learning-based, and deep learningbased methods can develop it.Due to massive datasets and improvements in deep learning-based methods, the systems have made significant progress.Sign language variation is a major issue due to regional dialects, signing styles, and context.Without standardized datasets or evaluation metrics, it is difficult to compare and benchmark SLR systems."PeoplesGroup" reported that there are 62,500 deaf individuals in Greece [1].Sign language is a nonverbal language characterized by specific hand gestures.The real challenge lies in the difficulty nondisabled individuals face with the grammar and rules of sign language.