CNN Based Speech and Text Translation Using Sign Language

There are around 5,00,000 deaf and hard of hearing persons in the globe. Around 60% of peoples are born as a deaf and deafeningly. These kinds of peoples are not able to understand anything like as normal peoples. The sign language (i.e., hand movements) are used for the dumb and deaf to pass their messages. These people’s faces lots of challenges on daily basis due to the relative absence of widespread sign language use in society. The helplessness to talk seems like true impairment. People with this defacement communicate in entirely several means. There are a variety of choices present for communication where sign language is one of the ways. Applications existence for sign language is necessary for dumb people so that they become able to talk easily with them who does not know sign language motions. The objective here is to take the essential acts in sustaining the language gap between conventional people and verbally challenged people. Communication becomes extremely difficult for them, the chance that they can talk is through language of sign. Though a dumb or deaf person might know language of sign, the people with whom she wishes to speak might not. Our aim to overcome this difference by using machine learning and neural networks to analyze sign language and translate it to text and voice.