Real-Time American Sign Language Recognition System Using Deep Learning and Computer Vision

– This paper presents a comprehensive real-time American Sign Language (ASL) recognition system that leverages deep learning and computer vision technologies to bridge communication gaps for the deaf and hard of hearing community. The system integrates MediaPipe for efficient hand landmark detection, OpenCV for image processing, and a custom Convolutional Neural Network (CNN) architecture implemented in TensorFlow for gesture classification. The solution employs an augmented AtoZ_3.1 dataset enhanced with geometric transformations, color variations, and noise-based augmentations to improve model robustness. The CNN architecture incorporates multiple convolutional layers with batch normalization, max pooling, dropout regularization, and a Softmax classifier, achieving over 98% validation accuracy on the ASL alphabet recognition task. The system features a PyQt5-based graphical user interface that provides real-time gesture visualization, text-to-speech conversion, word suggestions, and customizable themes. Multi-threading implementation ensures smooth user experience while maintaining prediction accuracy through temporal smoothing algorithms. Despite challenges including lighting sensitivity and single-hand gesture limitations, the system demonstrates significant potential for assistive communication applications and human-computer interaction interfaces.