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Abstract: Sign language serves as a primary mode of communication for the Deaf and hard-of-hearing community. This paper presents a Sign Language Recognition System (SLRS) designed to facilitate seamless communication between individuals proficient in sign language and those who may not share this proficiency. The system employs a multi-faceted approach, integrating computer vision, machine learning, and signal processing techniques to accurately interpret and recognize sign language gestures. The methodology involves data collection from diverse signing styles, preprocessing to enhance data quality, feature extraction capturing key aspects of sign language expressions, and the selection and training of machine learning models. The system aims to represent sign language gestures effectively, providing a foundation for real-time recognition. Integration into hardware or software platforms ensures practical applications in various settings, including education, employment, and public spaces.
Sukhadan et al. (Tue,) studied this question.