Deep Learning for Sign Language Interpretation with Conventional Neural Network

Deep Learning for Sign Language Interpretation With Conventional Neural Network project aims to create a sign language recognition system in real-time using CNNs, which includes voice output integration. The system can effectively identify hand movements that represent letters of the alphabet and translate them into spoken words, improving communication for those who cannot speak or have difficulty hearing. The recognition model is educated with a strong dataset of American sign language (ASL) and incorporated with TensorFlow Lite for efficient operation on mobile devices, guaranteeing portability and convenience. The user interface enables easy interaction, including the ability to add gestures, listen to them, add spaces, and delete the input. Instant translation of sign language gestures into spoken words is made possible with real-time processing, ensuring a smooth user experience. Although the system shows excellent precision in controlled environments, additional efforts are necessary to enhance its effectiveness in different settings, hand positions, and lighting situations. This project is a major advancement in making communication easier with sign language recognition and voice output.