A Novel Approach for Sign Language Detection Using Learning Algorithms

The study of sign language recognition has been a long-standing challenge. Nevertheless, our society still has a considerable distance to go before we uncover a full resolution. For many deaf and mute people, sign language is a vital form of communication. So, utilising the LSTM (Long Short-Term Memory) neural network algorithms, we built a model to recognise sign gestures. This model is also capable of detecting sign motions in complicated environments. Media pipe holistic has been used to evaluate the suggested model. In order to improve accuracy, we also incorporated several photos for training and testing. In order to achieve more accuracy, we compared our model against CNN (Convolution Neural Network). The proposed model is tested on real-time detection and obtained the precise results. Our paper discusses a technique to expedite the process of mapping sign language videos to corresponding sign writing visuals.