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

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.