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Author(s):

Prof.S. Poonguzhali | B. Bhavadharani | S. Dhanushiya | N. Hemala | N. Yasika
Keywords:

Sign Language Recognition, Deep Learning, Computer Vision, CNN, Natural Language Processing, Human-Computer Interaction, Assistive Technology
Abstract:
This Project Presents A Two-way Sign Language Translation System That Enables Communication Between Hearing-impaired Individuals And Non-sign Language Users. The System Converts Hand Gestures Into Text Using Computer Vision And Machine Learning Techniques, And Also Translates Text Into Sign Language Through Visual Representation. The Sign-to-text Module Uses Real-time Hand Detection And Classification To Recognize Gestures, While The Text-to-sign Module Generates Corresponding Sign Outputs. The Model Is Trained Using Labeled Gesture Data And Optimized For Accuracy And Speed. This Solution Helps Bridge The Communication Gap And Supports Inclusive Interaction In Everyday Environments.