Real-Time Sign Language Recognition and Translation: A Survey of Deep Learning Techniques

Abstract

Closing the communication gap between the Deaf and Hard-of-Hearing (DHH) community and hearing people has remained a key area of research in the recent past. This survey paper discusses the most current deep learning methods applied in Sign Language Recognition (SLR) and Sign Language Translation (SLT) systems. The paper examines the transition from the traditional vision-based methods to more recent neural models, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Transformer models such as Sign Language Transformers (SLT). Hybrid models that integrate CNN and LSTM have scored remarkable accuracy rates of more than 90% in static as well as dynamic gestures. The latest YOLO and MediaPipe frameworks enable real-time detection of hand gestures through simple webcams. The paper also compares various datasets, performance metrics, and preprocessing techniques utilized for several regional sign languages, such as Indian, Arabic, and American Sign Languages. Challenges still remain in continuous sign recognition, signer variation, and dataset variation despite the major advancements. This work recommends directions for the development of fully end-to-end, real-time, and multilingual sign language interpretation systems toward inclusive humancomputer interaction.