This project aims to address the sign language detection task by leveraging the power of Action Recognition with Python LSTM Deep Learning Model. Action recognition, a specialized area within computer vision, focuses on recognizing and classifying human actions or activities from video sequences. Long Short-Term Memory (LSTM), a type of recurrent neural network (RNN), plays a crucial role in capturing temporal dependencies in sequential data, making it well-suited for sign language recognition.
Through this project, we delve into the world of deep learning and LSTM to build an accurate and efficient model for sign language detection. By training the LSTM model on a labeled dataset of sign language videos, the model can learn to interpret various hand gestures and movements, enabling it to recognize different sign language expressions and translate them into text.