Sign Language Detection and Translation Using Smart Glove

Communication through sign language is essential for hard of hearing or deaf people. However, effective interaction and involvement in many facets of society are hampered by the lack of communication that exists between signers and non-signers. In order to solve this pressing problem, a smart glove-based system for real-time detection and translation of sign language has been developed as part of this research project. The project’s goals include designing and creating a smart glove prototype that has an MPU-6050 and five flex sensors. Intricate finger and hand movements, which are essential to sign language, are precisely captured by these sensors. To handle the sensor input, a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) is used, which recognizes and distinguishes different gestures. The system’s first focus is on American Sign Language (ASL) gestures that correlate to the English letters A through E and the numerals 0 through 9. The real-time translation of gestures into speech and text is a significant innovation. A text-to-speech engine is integrated into the software, enabling simultaneous textual and audio outputs. Sign gestures made by users are translated into text on a screen instantaneously and used to communicate with the system. To facilitate effective communication with both signers and non-signers, the system simultaneously interprets the text into spoken language. The primary focus of the research project is alphabet and number recognition and translation. It skips over how to recognize convoluted sign language statements or how sign languages differ from one another. Due to time and budget constraints, evaluation focuses on accuracy and performance measures instead of in-depth user research.