A Review of Artificial Intelligence-Based Real-Time Sign Language Recognition Using Computer Vision

Abstract

Sign Language Recognition serves as an essential tool to bridge the communication gap between hearing and Deaf communities. This paper presents a comprehensive review of AI-based sign language recognition models for real-time applications using computer vi sion. The study explores recent advances incorporating Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and transformer architectures, analyzing 20 research articles published within the last two years to identify current methodologies, performance metrics, and implementation trends. Analysis reveals that spatial-temporal learning models achieve supervised learning accuracy exceeding 95% for isolated sign recognition and 89-96% for continuous sign sequences. Skeleton-based Graph Convolutional Networks demonstrate superior performance (96.1% accuracy) compared to RGB-based methods, while multimodal fusion strategies yield 2-8 percentage point improvements over unimodal approaches. However, significant challenges persist in practical deployment, including environmental robustness (10-25% accuracy degradation across different settings), signer variability, continuous sign segmentation (accuracy drops from 80-92% to 65-75% in natural streams), and limited dataset vocabulary coverage (5-10% of full sign language dictionaries).This review contributes a systematic synthesis of state-of-the-art techniques, identifies critical implementation barriers, and provides researchers and practitioners with clear directions for advancing real-time sign language recognition toward practical, inclusive communication systems.