Advancing Communication for the Hearing Impaired: Real-Time Sign Language Recognition and Translation

Sign language detection systems effectively bridge the gap between the deaf handicapped community and general society, providing a way for non-verbal individuals to express themselves inclusively. This paper presents for the first time automatic sign language recognition based on techniques in computer vision, real-time hand tracking, and machine learning to facilitate accurate and responsive gesture interpretation. Our approach is based on a computer vision framework that captures and processes hand gestures in real-time for the dynamic detection and classification of signs while performing them. It is this work that takes precedence regarding accessibility and enabling a close interaction between sign language users and others in varying settings-from the classroom to a customer interface. The advanced hand detection system is employed to capture very intricate hand movements and subtle gestures. These are very critical for distinguishing between signs that are visually similar but denote different meanings. By employing high-resolution image preprocessing techniques, we ensure that the input data is clean, consistent, and highly conducive to classification. These preprocessing actions serve to normalize each gesture frame to compensate for the variability imparted into the model by hand shape, orientation, and to some extent leaking one hand over the other. The focus of our approach presents the machine learning model trained under Keras to classify gestures very accurately. We trained our model on a very strong data set of sign language gestures, allowing it to learn the subtleties of hand shapes, movements, and transitions that denote each sign. During testing, we noticed that because of its fine-grained feature detection, these models can differentiate among similarly seeming gestures that may just differ by small changes in hand orientation or finger placements.