A Vision-Based Computational Recognition Using MediaPipe Hand Landmarks and CNN

The “Vision-Based Computational Recognition Using MediaPipe Hand Landmarks and CNN” system is designed to bridge the communication gap between hearing-impaired and non-signing individuals through real-time sign language recognition. The proposed framework employs MediaPipe for efficient hand landmark extraction and a Convolutional Neural Network (CNN) for accurate gesture classification. The system translates American Sign Language (ASL) gestures into text and speech, providing a seamless and accessible mode of interaction. By combining real-time hand tracking, feature extraction, and deep learning-based recognition, the framework achieves high accuracy and low latency even under varying lighting and background conditions. This solution not only enhances communication accessibility but also supports inclusive human–computer interaction, enabling practical applications in education, healthcare, and assistive technologies.