Real-time sign language recognition based on neural network architecture
Priyanka Mekala, Ying Gao, Jeffrey Fan, Asad Davari · 2011
In real-time, it is highly essential to have an autonomous translator that can process the images and recognize the signs very fast at the speed of streaming images. In this paper, architecture is being proposed using the neural networks identification and tracking to translate the sign language to a voice/text format. Introduction of Point of Interest (POI) and track point provides novelty and reduces the storage memory requirement.