HAND GESTURE RECOGNITION FOR DEAF AND DUMP USING CNN TECHNIQUES (KEY IJP************974)

• N.devi Sri,C.g.karunya Shree Nee,K.kavipriya,R.keerthana,K.steephen

Abstract

Sign language is a type of communication used by people with hearing and speech impairments. Disabled People utilise these sign language gestures as a form of non-verbal communication to convey their own feelings and ideas to other regular people. It can be quite difficult to communicate with persons who have hearing loss. Since Deaf and Mute persons communicate via hand gestures, normal people have difficulty understanding the signs they make. Systems that can identify various indications and provide information to common people are therefore necessary. However, these common people find it difficult to interpret their expression, thus qualified sign language experts are required during medical and legal appointments, as well as educational and training sessions. The demand for these services has grown during the previous few years. Other types of services have been established, such as video remote human interpretation using a high-speed Internet connection. These services offer a simple sign language interpretation service that can be used and is beneficial, but has significant drawbacks. We can employ artificial intelligence technology to examine the user's hand with finger detection in order to solve this issue. We may construct the vision-based system in real-time situations using the proposed technology. Convolutional neural network algorithm, a deep learning method, is then used to classify the sign and provide a label regarding recognised sign.