Sign Language to Text and Speech Conversion Using Machine Learning

On a daily basis, deaf people face major communication barriers.They find it challenging to interact with those who do not understand sign language since they are deaf.Additionally, it poses challenges in social, professional, and educational settings.Technology can help overcome these challenges by offering alternate avenues of communication.Sign language recognition is one example of a technology that can help hearing and deaf people communicate.To translate Indian Sign Language to speech or text, we will develop a strong sign language recognition system.We will compare the CNN and LSTM models and assess the suggested system.A strong model is needed to differentiate between static and gesture sign languages since they exist.Since it will improve the communication abilities of the deaf and hard-of-hearing population that depends on sign language, the development of a text-to-sign language paradigm is crucial.Not everyone who is deaf or hard of hearing is skilled at reading or writing text, even if sign-to-text translation is only one aspect of communication.Some people may struggle to understand written language because of literacy or educational challenges.They would thus be able to understand text-based information and engage in a range of social, educational, and professional contexts with the help of a text-to-sign language paradigm.This study uses a production system to help deaf and hearing people communicate and attempts to enhance sign language identification.The deaf can benefit from technology and find it easier to integrate into society.