Hand gesture recognition for sign language translation

This project aims to develop software that translates Indian Sign Language (ISL) hand gestures into real-time text and speech. A custom symbol module enables users to add new gestures, enhancing user experience. Voice call integration allows real-time gesture-to-speech translation during calls. Optimized machine learning models ensure efficiency and accessibility, creating a solution for inclusive communication. The application relies on optimized machine learning models from TensorFlow Lite which enables safe gesture recognition across different mobile devices. The application needs this specific technology due to its power to maximize accessibility and usability because it operates well on devices with limited processing capacity. The system's architecture puts inclusivity first with its purpose to create an accessible communication tool which serves the deaf and hard-of-hearing community through user-friendly functions. The implementation of thorough error handling and feedback functions in the software system creates improved user experience along with better system dependability. The initiative resolves the current weaknesses of sign language translation solutions through its personalized and efficient software which is accessible to users. A modernized translation framework creates new possibilities for better quality of life among ISL users because it promotes fluid and natural speaking opportunities across different situations. This system includes scalability features to enable future adjustments according to developing requirements.

Keywords: Customizable gestures, gesture recognition, Indian Sign Language, machine learning, real-time communication, voice call integration.