Sign Language 2.0: Rethinking ASL Education for the Modern Age

American Sign Language is a non-verbal communication language that uses the motion of the hand and changes in hand shape to convey its meaning. It is the main mode of communication for those with hearing and language impairments. Sign language recognition helps a large number of people to communicate with regular people but there is a need to build an easy approach for these people so that they can learn in a better way and enhance their lives. This research focuses on the creation of a unique deep learning-based system for the recognition of American Sign Language (ASL). This system identifies 21 separate hand landmark coordinates by using Media Pipe to capture the complex dynamics of hand movements that are required for exact ASL communication. Recall, F1 Score, and Precision are used to validate the results of the experiments. The trained Deep Learning models have high generalization abilities with 98.2% classification accuracy towards 26 letters in the alphabet.