Sign language : a systematic review on classification and recognition

Sign language serves as an alternative communication mode for those with limitations in hearing and speaking abilities. In India, an estimated population of around 2.7 million people have hearing or speech impairments. Among this population, a significant majority of 98% use sign language as their primary mode of communication. Unfortunately, the limited availability of human interpreters is a considerable obstacle in recognizing and identifying diverse sign languages. To tackle this issue, the present study aims to undertake a thorough examination of various computational methodologies used in various geographical areas for the purpose of categorizing and identifying discrete sign languages. Among the pool of 587 research papers deemed appropriate for qualitative assessment, a total of 95 studies were specifically focused on the categorization and identification of sign language using artificial intelligence-based techniques. The study examines prior research involving deep learning and machine learning methods for a systematic literature review of Sign Language Recognition (SLR). The categorization is made based on language, and the study investigates several facets, including sign type, signing modes, processing techniques, classification methodologies, and evaluation measures. The study reveals that extensive studies were carried out in Chinese, Arabic, and American sign languages. The findings of this review show that among the machine learning techniques, Support Vector Machine (SVM) exhibited higher performance measures, while Convolutional Neural Networks (CNN) exhibited higher performance among the deep learning techniques.