Research interests rooted in machine learning, accessibility, and engineering systems.

Real-Time Detection and Translation of Bangla Sign Language Characters Using Deep Learning

This study demonstrates the effectiveness of Google Teachable Machine in developing an accessible and accurate Bangla Sign Language recognition system. The system's reliability is evident in its nearflawless classification across all character categories, with minimal errors occurring only between visually similar signs. Implementation as a web application through the Flask framework enhances real-world utility by offering immediate translation via camera input, visual confirmation of recognised signs, and integrated learning resources. This combination of features addresses critical needs for both communication assistance and language education within the Bangla-speaking deaf community. The solution proves that assistive technology can maintain high accuracy standards while remaining broadly accessible, particularly important for underrepresented languages where complex systems often face adoption challenges.