Sri Lankan Tamil Sign Language Recognition Using Machine Learning and Computer Vision: A Systematic Literature Review

Abstract

Sign language recognition has become an important research area in assistive technology, with machine learning and computer vision providing opportunities to support communication between deaf or hard-of-hearing people and the wider community. Existing studies have investigated different approaches for recognizing sign language gestures from images and video, including convolutional neural networks (CNNs), long short-term memory (LSTM) networks, support vector machines (SVMs), wearable devices, depth cameras, and mobile-based systems. However, much of the existing research focuses on widely studied sign languages or Sinhala Sign Language, while research specifically related to Sri Lankan Tamil Sign Language (TSL) remains limited. Therefore, this systematic literature review examines existing machine learning and computer vision approaches for sign language recognition and translation, with particular attention to their relevance to TSL. A total of 18 studies were reviewed based on the sign language considered, type of input data, recognition task, machine learning technique, dataset, translation approach, hardware requirements, and reported limitations. The review shows that CNN-based approaches are commonly used for visual gesture recognition, while LSTM and other sequence-based approaches have been applied to continuous sign recognition. SVM- based methods and specialized hardware such as depth cameras and wearable devices have also been reported. However, the reviewed studies indicate limited availability of TSL-specific datasets and limited research on continuous TSL recognition, Tamil language translation, and low-cost real-time implementation. These findings indicate the need for further TSL-specific research, particularly the development of suitable datasets and the evaluation of computer vision and machine learning approaches using TSL data. The findings of this review provide a basis for future research on practical TSL recognition and translation systems using affordable camera-based devices.