Sign Language Recognition System

The goal of vision-based sign language recognition is to improve communication for the hearing impaired. However, the majority of the available sign language datasets are constrained. Real-time hand sign language identification is a problem in the world of computer vision due to factors including hand occlusion, rapid hand movement, and complicated backgrounds. In this study, we develop a deep learning-based architecture for effective sign language recognition using Single Shot Detector (SSD), 2D Convolutional Neural Network (2DCNN), 3D Convolutional Neural Network (3DCNN), and Long Short-Term Memory (LSTM) from Depth and RGB input films.