Multimodal Deep Neural Networks for Robust Sign Language Translation in Real-World Environments

Background: Sign language recognition aims to convert hand gestures used in sign language into spoken or written words, facilitating communication between hearing and deaf individuals. This technology has great potential to enhance accessibility and foster inclusivity for individuals who are deaf or hard-of-hearing. Various techniques can be employed for sign language recognition, including Computer vision algorithms for analyzing visual data captured by cameras, sensor-based approaches that utilize sensors attached to the hands or fingers to record motion data, and hybrid approaches that combine both methods Our research results imply that a hybrid approach combining computer vision and sensor-based techniques yields the most accurate and robust sign language recognition system. By leveraging the strengths of both methods, the hybrid approach can effectively capture and interpret intricate hand and finger movements, resulting in a higher recognition accuracy rate compared to using either technique independently. Developing precise and reliable sign language recognition systems. has the potential to revolutionize communication and accessibility for individuals with hearing loss. Our proposed hybrid approach offers a promising solution for overcoming the challenges associated with sign language recognition, paving the way for bridging the communication gap and promoting inclusivity for the deaf and hard-of-hearing community.