Comparative Analysis of Multiple ML Models and Real-Time Translation

A huge population in the present world is suffering from hearing impairment. This hinders their ability to communicate and express their opinions. Implementing an accurate gesture capture and interpretation system through an image classification model can significantly reduce the impact of hearing impairment, enabling effective recognition of the user's gestures. The proposed project develops machine learning models based on computer vision and image processing to capture and interpret the hand gestures of differently-abled people. Various image classification algorithms are studied, and a comparative analysis using predefined evaluation parameters identifies the most effective solution for gesture recognition for individuals with hearing impairments. A real-time live video implementation model to recognize certain regularly used gestures is also executed. This video implementation will detect some of the frequently used signs, enabling easier communication for individuals with hearing impairments and muteness. The proposed approach is versatile, with applications in device control and user feedback.