Paper Contents
Abstract
Sign language recognition (SLR) has gained significant attention in recent years due to the increasing need for inclusive communication technologies for individuals with hearing and speech impairments. Traditional sign language recognition systems rely on sensor-based hardware such as gloves and motion trackers, which can be costly and inconvenient. Recent advancements in artificial intelligence (AI) and deep learning have enabled real-time, vision- based sign language recognition systems using convolutional neural networks (CNNs) and recurrent neural networks (RNNs). This research proposes a real-time sign language recognition system leveraging deep learning and computer vision techniques to translate hand gestures into meaningful text or speech.
Copyright
Copyright © 2025 Shreedatt Pandya. This is an open access article distributed under the Creative Commons Attribution License.
