Sign Net: A Deep Learning Approach Towards Efficient ASL Recognition

Abstract

The use of Convolutional Neural Networks (CNNs) for real-time detection and interpretation of American Sign Language (ASL) signifies a breakthrough in technological advancements. By bridging communication barriers, CNNs have emerged as an innovative solution fostering inclusivity. CNNs, a deep learning neural network, are employed to recognize and comprehend the subtleties in sign sequences, translating them into a language of numbers, matrices, and vectors. The process involves scanning each frame of a video feed, detecting recurring structural elements using filters, and refining the broader patterns to identify minute details like finger curvature, thumb angle, or palm orientation. With modern high-speed processors, real-time translation of these signs into written or spoken language is feasible. This significant accessibility enhancement opens up possibilities for incorporating this technology into smart devices, public information systems, and educational platforms, thereby promoting inclusive communication. Factors like variations in sign generation, sign language communication speed, contextual nuances, ASL dialects, individual signer eccentricities, and the necessity for robust datasets for training deep learning models, pose significant hurdles. With continually developing algorithms, increasing computational resources, and expanding diversified datasets, the potential for applying CNNs for real-time ASL identification is immense.