Real-Time Implementation of an AI-Based Virtual Sign Language Recognition and Interpretation System

The Deaf and Hard of Hearing (DHH) community faces significant communication challenges in various sectors, especially in healthcare settings where effective communication is crucial for proper diagnosis and treatment. Communication barriers between healthcare providers and DHH patients are many and varied, leading to misunderstandings, misdiagnoses and inadequate care. Traditional methods, such as written notes or relying on family members to interpret, are often inadequate and can compromise DHH patients’ safety and quality of care. As Sign language is the natural language of DHH people is sign language, we propose a real-time virtual sign language recognition and interpretation solution powered by Artificial Intelligence (AI) and machine learning algorithms. This solution aims to bridge the communication gap, ensuring innovation, accessibility and accuracy. In our method, we train the Convolutional Neural Networks (CNNs) using a diverse dataset of sign language images. This enables the system to proficiently recognize and interpret a wide range of sign language gestures in real-time. To further enhance the system’s recognition capabilities, we employ data mining techniques, which continuously refine the model’s accuracy and inclusivity. Our study demonstrates impressive accuracy, making the virtual interpreter suitable for real-time healthcare applications. Instant sign language translation, Empowers DHH individuals to express themselves and understand medical information, fostering clearer communication with healthcare providers, and thus improving health outcomes and DHH patient satisfaction while creating a more inclusive healthcare system. Moreover, this technology extends beyond healthcare, promoting inclusivity and breaking communication barriers in various aspects of life, representing a significant step towards building a more inclusive society that empowers DHH people through AI.