Real-time Gesture Recognition for Sign Language Using Machine Learning

A Real time gesture recognition system employs and leverages on the concepts of Machine learning to interpret and respond promptly to the user gestures, significantly enhancing the Human Computer Interaction. Instead of analyzing one frame and predicting the gesture, we use a set of frames to determine the action. Media Pipe is used for the process for key point extraction of the hand joints, which provides comprehensive data for the analysis. For the prediction in real time, the Long Short Term Memory (LSTM) model is trained using TensorFlow. Keras is used for building and training neural networks in Python, alongside TensorFlow to streamline the model development process. This system aims to have seamless interaction between the machine learning model and the gesture data extraction to enhance the user experience and opens up new possibilities for intuitive and natural communication with computers.