Automatic Sign Language Recognition Using Convolutional Neural Networks

Using machine learning methods, the project seeks to construct a system that can convert between sign language and spoken language. Step one is to gather a large and varied datasets of sign language motions. Step two is to prepare the data for annotation. The labeled dataset is used to train a Recurrent Neural Network or Convolutional Neural Network, two popular machine learning models. A camera or wearable devices may be used to input sign language into the system. The output is transformed into text by means of the trained model, which anticipates sign motions. To make it more practical, an interface for users may be included. To make the system more accurate and accessible to more people, it is essential to optimize and develop it continuously. User input and community interaction play a big part in this process. Cameras or wearable's feed sign language into the system, and a trained algorithm anticipates motions and turns them into text. Practical applications may make advantage of user- friendly interfaces. In order to make the sign language translation process more accurate and accessible, we emphasize continuous improvement, user input, and community interaction.