Continuous Recognition of Sign Language

The goal of this project is to develop and train a deep neural network for continuous recognition of sign language. This network would be used to enable communication between deaf and hearing people, as well as to create a sign language to text translation system.

To achieve this goal, we will use a convolutional neural network (CNN) and a recurrent neural network (RNN) to build a deep learning model. The CNN will be used to extract features from the images of the sign language and the RNN will be used to recognize the sequence of gestures. We will also use a Long Short-Term Memory (LSTM) to capture the temporal information.

Our dataset will consist of images of sign language gestures captured from a video stream. We will use a combination of supervised learning and reinforcement learning to train the deep neural network. For supervised learning, we will label the images with the corresponding sign language gestures. We will also use reinforcement learning to optimize the performance of the network.

We will evaluate the performance of our model by measuring its accuracy on a test set of images. We will also compare our model to existing models for sign language recognition. The results of this project will provide a solution for enabling communication between deaf and hearing people, as well as

Applications:

1. Enables communication between deaf and hearing people
2. Create a sign language to text translation system
3. Recognize and interpret sign language in real-time, allowing for more efficient communication.