Real Time Hand Gesture Recognition for Sign Language Communication by Using AI & ML

GestureSync Pro is a real-time hand gesture recognition system designed to bridge the communication gap between sign language users and the general public. The system utilizes computer vision and deep learning techniques to recognize American Sign Language (ASL) gestures and convert them into meaningful text and speech output.

A webcam is used to capture live video input, and MediaPipe is employed to extract hand landmarks for efficient feature representation. A Convolutional Neural Network (CNN) model is trained on a large dataset of hand gestures to accurately classify ASL alphabets. The system further integrates heuristic logic and a hold-to-confirm mechanism to improve prediction stability and reduce false detections.

To enhance usability, the recognized gestures are processed using AI-based sentence generation to produce grammatically correct outputs, which are then converted into speech using a real-time speech synthesis module. The model is deployed using TensorFlow.js, enabling fast and efficient inference directly in the browser.

GestureSync Pro provides an accessible, cost-effective, and real-time solution for sign language communication, with potential applications in education, healthcare, and human-computer interaction.

Artificial Intelligence, Computer Vision, Convolutional Neural Network (CNN), Deep Learning, Gesture Recognition

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