Enhancing Communication: Real-Time Sign Language Detection with Deep Learning and Python

Deaf and hard-of-hearing people use sign language detection to communicate. Sign language is crucial for deaf and hard of hearing people to communicate. Recent advances in computer vision and machine learning have enabled sign language gesture recognition and decipherment. Abstract: Deep learning and computer vision techniques for sign language identification systems are studied and developed. Insufficient datasets and regional sign language gesture variations are discussed in this research. The suggested methods improve sign language recognition system precision and responsiveness, improving deaf community accessibility and inclusivity. Deploying the model on powerful hardware and using TensorFlow's GPU support allows low-latency sign identification for real-world applications. Our experiments show that the system can recognize sign motions in real time with high accuracy and minimal latency. Deaf and hard-of-hearing people can communicate and live better with this technology, making sign language more inclusive. 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).