System to Enhance Communication in Health Care by Converting Sign Language into Spoken Language using ML

This research paper focuses on the development of a real time system for the conversion of sign language into text and speech, to improve communication in health care. The proposed system involves use of Convolutional Neural Network (CNN), image processing, natural language processing and gesture recognition to achieve accurate and efficient conversion between sign language and spoken language. CNN is essential for identifying sign language gestures from real time inputs because it has been extensively trained on a variety of datasets. This deep learning approach enhances the system’s ability to recognize a wide range of gestures with very high accuracy. The system also includes speech synthesis, which provides real-time vocalization of the translated sign language, making it more accessible for individuals with hearing impairments to communicate seamlessly with healthcare providers. Furthermore, the system's adaptability allows it to learn from new inputs over time, continuously improving the accuracy and efficiency of the translation process. The proposed solution aims to bridge the communication gap between healthcare professionals and patients who use sign language, thereby promoting more inclusive, effective, and patient-centered care. Real-time sign language translation systems are of paramount importance in enabling communication for deaf and hard-of-hearing individuals. This population relies on various communication methods, including sign languages and visual techniques, to interact with others. While assistive technologies, such as hearing aids and captioning, have improved their communication capabilities, a significant communication gap still exists between sign language users and nonusers. In order to bridge this gap, numerous sign language translation systems have been developed, encompassing sign language recognition and gesture-based controls.