Sign Language Recognition Using Keypoints Through DTW

Hearing-impaired people utilize hand signals, which is an organized collection of hand gestures with distinct meanings. Despite the fact that automated Sign Language Recognition(SLR) is essential for these people, the focus of the research is still challenging and mostly unexplored. This paper is focused on an implemented system for recognizing sign language using SLR system. For numerous computer vision that help to increase communications among hearing impaired, deaf and dumb the sign language identification is a exciting and crucial research area. This research, offers a model with Dynamic Time Wrapping(DTW) approach for recognizing signs in video clips. This innovative technique uses body and hand skeletal features that are derived from RGB movies to capture highly discriminative skeletal data for gesture identification. Experiments on a sizable sign language videos shows that this methodology is better than other cutting-edge methods that just use RGB features. The holistic features extracted as connected key points to identify sign language of the image and accuracy of the results are analyzed.