A Survey on Sign Language Recognition and Training Module

Abstract

Communication among the deaf and non-verbal communities has long reliedon sign language recognition. From all researchers around from early electric signal-based sign language identification to more recent recognition using machine/deep learning techniques, the globe has tried to automate this process. The main objective of this research is Recognition of sign language based on key point detection (SLR).American Sign Language (ASL), primarily ASL pickle data, is the subject of this work. The model was trained using a variety of machine learning algorithms, including randomforest, support vector machine, and k closest neighbor. Lastly, utilizing evaluation criteria such as f1score, precision, and recall, the best model is chosen from the model testing. A straightforward GUI is created to collect user input, and the best machine learning model makes the forecast. Also, a Training tool is created for the purpose of learning the American sign language which will create a major difference for non-verbalcommunities