Junhan Wang¹ ans Jonathan Sahagun², ¹USA, ²California State Polytechnic University, USA

In this paper, we tackle the pressing communication gap between the Deaf and hearing communities, an issue affecting millions of individuals worldwide [1][2]. The proposed solution is a machine learning-powered application that translates sign language into text in real-time, allowing Deaf and hearing individuals to communicate directly [3]. The development faced challenges such as acquiring a diverse and accurate dataset and managing real-time processing of gestures. Experimentation involved testing the model's accuracy across multiple users, revealing promising outcomes. Two existing methodologies, a sensor-based glove and a single-camera solution, were compared, highlighting areas for potential enhancement in our approach. Despite the diversity of sign languages and their unique grammatical structures, the project represents a significant step towards more accessible communication. It highlights the potential for further advancement in machine learning applications for translation and inclusivity.

Artificial Intelligence, Machine Learning, Gesture Recognition, Sign Language Translation