Harnessing AIML for Real-Time Conversion of Sign Language to Improve Web Accessibility

Access to information is a crucial aspect of modern digital life, particularly for individuals who rely on sign language for communication. This paper introduces a real-time extension that translates sign language gestures into text, providing an inclusive solution for individuals with speech disabilities. The system uses advanced computer vision and deep learning techniques, employing a Convolutional Neural Network (CNN) trained on a diverse dataset of American Sign Language (ASL) gestures. The technology records hand movements using a webcam, processes them in real time, and converts them into textual data for seamless online interactions, including web searches. This solution mirrors the simplicity and convenience of voice-command systems, offering a user-friendly and accessible interface. The innovation lies in its incorporating gesture recognition with web interaction capabilities, achieving notable improvements in usability and accuracy. With validation results exceeding 99% accuracy, the system demonstrates robustness under various conditions, bridging communication gaps and clearing the path for future developments, including multilingual sign language support and expanded accessibility features.