Ai-Based Hospital Assistance System Using Indian Sign Language Translation

This research presents a hospital assistance framework that uses AI to enable smooth communication between patients and reception staff. The system recognizes Indian Sign Language (ISL) in real-time and translates speech and text. This helps guide patients effectively without needing a human interpreter. The framework allows for two-way communication: patients use ISL gestures, which are translated into text or voice for the receptionist. In turn, the receptionist\\\'s responses convert back into ISL animations displayed to the patient. The model uses Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) architectures, along with a Connectionist Temporal Classification (CTC) decoder for aligning sequences. The preprocessing pipeline uses MediaPipe and OpenCV to extract hand landmarks and reduce noise. A dataset with healthcare-related gestures, such as "doctor," "appointment," "medicine," and "wait," trained the model. The system operates fully on software and does not require specialized hardware. This solution offers an efficient and accessible way for guiding patients through hospital services, ensuring inclusivity and improving communication at the reception desk.