Real-Time Sign Language Detection with Deep Learning and Computer Vision Improves Deaf and Hard-of-Hearing Accessibility

Abstract

Deaf and hard-of-hearing people use sign language detection to communicate. Communicating with the deaf or hard of hearing requires sign language recognition. Recent advances in computer vision and machine learning have enabled sign language gesture recognition and decipherment. Sign language identification systems using deep learning and computer vision methods are investigated and developed in this abstract. The study highlights research challenges such dataset shortages and regional sign language gesture variances. The suggested methods improve sign language recognition systems' precision and responsiveness, improving deaf community accessibility and inclusivity. Low-latency sign recognition is possible for real-world applications by running the model on powerful hardware and using TensorFlow's GPU support. Our experiments show that the system can recognise sign motions in real time with high accuracy and minimal latency. This technology could make sign language more accessible and inclusive for deaf and hard-of-hearing people. Deep Learning, TensorFlow, CNN, Real-Time, Gesture Recognition, Video Processing, Machine Learning, Low Latency, Human-Computer Interaction, Diverse Sign Language Dataset, RNN are used in Sign Language Detection (SLD).