Gesture Recognition for Phrase Interpretation: A Deep Learning Approach

Gesture recognition holds paramount significance in facilitating communication for individuals utilizing sign language to convey phrases and expressions. We present an innovative approach to gesture recognition in this article tailored for interpreting hand signs to derive meaningful phrases, such as “I love you,” “please,” “sorry,” and “goodbye.” Our proposed methodology integrates a multi-box detector (SSD) with dynamic hand gesture analysis to achieve real-time and accurate recognition of hand signs across various languages and cultural contexts. By harnessing the power of deep learning SSD for object detection, our system ensures swift responses while maintaining precision. Additionally, the incorporation of dynamic hand gesture analysis techniques enhances the system's adaptability to diverse sign styles and linguistic nuances, thus fostering better communication access. By means of meticulous experimentation, we establish the effectiveness of our methodology in precisely identifying and deciphering real-time sign language gestures, attaining noteworthy results in terms of both speed and accuracy. This study enhances assistive technology for hard-of-hearing and deaf communities, thereby promoting accessibility and inclusiveness in communication environments that are accommodated to the linguistic requirements of a variety of individuals.