Inflated 3D ConvNet for detection of sign language

Abstract

Every human has their own kind of disabilities, we all try to live and overcome them in our life. We educate ourselves to overcome them, we invent technology to achieve our goals. Sign Language is a communication path for deaf-mute people through hand gestures and actions. Sign Language helps people who can't speak sign language to interact with the people who can speak sign language, this deep learning paper aims to help build a communication bridge for this reason. We used Amazon Rekognition service which uses Deep CNN algorithm for the detection of static images of the signs. As most of the signs are for words they are in the form of videos. We used the I3D algorithm for the classification of videos of the signs of words. The PyTorch framework provides support for CuDNN (NVIDIA CUDA Deep Neural Network) which provides fast GPU implementations for the deep neural networks. The Experimental results has shown that the models used has displayed good results in detecting the words.