Sign Language Recognition and Translation System Using Deep Learning Project

Communication barriers often create challenges for individuals with hearing and speech impairments. This project proposes an intelligent Sign Language Recognition and Translation System that utilizes Deep Learning, Computer Vision, and Natural Language Processing to facilitate seamless communication. The system captures hand gestures using a webcam and recognizes sign language alphabets in real time through a trained Convolutional Neural Network (CNN) model. Recognized signs are converted into text, while user-entered text can be transformed into sign language images and animated gestures. The application also incorporates NLP techniques to simplify sentences and map words to corresponding sign animations. Additionally, users can learn sign language through interactive learning modules. Developed using Django, TensorFlow, OpenCV, and SpaCy, the system provides an effective and accessible communication platform for hearing-impaired individuals.