ISL Recognition of Emergency Words Using MediaPipe, CNN and LSTM

This research paper focuses on the development, improvement and evaluation of a machine learning model for recognizing Indian Sign Language (ISL) hand gestures in emergency situations. By combining computer vision and machine learning techniques, the system enables real-time identification and classification of ISL hand gestures, facilitating effective communication between emergency responders and individuals who use ISL. The system has been rigorously tested and demonstrates high accuracy and reliability in various emergency scenarios. The findings of this study highlight the potential of hand gestures as a powerful tool for communication in emergency situations, offering valuable insights for the advancement of inclusive and responsive emergency response systems.