A Review on Advances in Indian Sign Language Recognition: Techniques, Models, and Applications

Abstract

Abstract: Sign language serves as a crucial medium of communication for individuals with hearing and speech impairments, yet it presents barriers for those unfamiliar with its nuances. Recent advancements in artificial intelligence, computer vision, and deep learning have paved the way for innovative sign language recognition (SLR) systems. This paper provides a comprehensive survey of cutting-edge approaches to real-time sign language recognition, with a specific focus on Indian Sign Language (ISL). Various methodologies, including Convolutional Neural Networks (CNNs), Hidden Markov Models (HMMs), and transfer learning, are explored to evaluate their performance in recognizing static hand poses and dynamic gestures. Systems leveraging grid-based features, skin color segmentation, and feature extraction techniques such as Histogram of Oriented Gradients (HOG) are examined for their accuracy and efficiency. Furthermore, the integration of advanced frameworks, including pre-trained models like ResNet-50 and novel pipelines combining gesture reclassification with text-to-gesture synthesis, is discussed. This survey emphasizes the role of machine learning models, from Random Forests to state-of-the-art Large Language Models (LLMs), in addressing linguistic variability and enabling cross-linguistic translations between sign languages. By consolidating these diverse techniques, this study aims to provide valuable insights into the development of robust, real-time SLR systems that enhance accessibility, social integration, and inclusivity for the hearing-impaired community.