Achieving precision, quick processing, and adaptability across different surroundings remains a major challenge for gesture recognition. This work introduces a vision-based framework designed to translate static hand signs into text while keeping computational effort minimal. The framework employs OpenCV for capturing and preprocessing input, cvzone for hand tracking, and a CNN model for classification. Each captured gesture is standardized and mapped to a letter, enabling direct text conversion. The approach is particularly helpful for people with hearing or speech difficulties and also finds applications in robotics, education, and immersive technologies. Testing confirms strong accuracy with very low delay during live operation.
Ms. Vijayalakshmi S (Thu,) studied this question.