Gesture Language Recognition Through Deep Learning

Speech and hearing-impaired individuals use gesture-based sign language as their chief method of communication. This sign language helps them to convey their feelings and thoughts to others. Despite this, interaction between the hearing-impaired and those who cannot understand sign language is still a major issue. Hand gestures are used by deaf and mute individuals for communication, which others might find challenging to understand. Thus, systems that can identify and interpret sign language into a form that the general public can understand are imperative. Sign language interpreters are currently needed in many situations, such as medical and legal appointments, educational sessions, and training sessions. Still, access to trained individuals is not always available. To mitigate this issue, we suggest a real-time, vision-based system for sign language recognition. Moreover, we advance the framework to record spoken language and translate it into text through the use of the Hidden Markov Model. Lastly, we use Natural Language Processing (NLP) methods to project sign-based text recognition using AVATARS, promoting accessibility and communication.