Sign Language Detection Using Deep Learning

Sign language recognition is an essential tool for bridging communication gaps between individuals with hearing or speech impairments and the broader community. This study introduces an advanced sign language recognition system leveraging computer vision and machine learning techniques. The system utilizes real-time hand tracking and gesture recognition to identify and classify hand gestures associated with common phrases such as "Hello," "I love you," and "Thank you." A two-step approach is implemented: first, a data collection module captures hand images using a robust preprocessing pipeline, ensuring uniformity in image size and quality; second, a classification module uses a trained deep learning model to accurately predict gestures in real-time. The framework integrates OpenCV for image processing, CVZone modules for hand detection, and TensorFlow for gesture classification. Extensive testing demonstrates the system's capability to process live video input, classify gestures accurately, and display corresponding labels seamlessly. This solution addresses challenges in gesture recognition, such as variable hand shapes and dynamic backgrounds, through efficient preprocessing and model training. By offering a scalable and efficient design, this work has the potential to contribute significantly to assistive technologies and accessible communication systems, paving the way for further advancements in human-computer interaction and inclusive technology.