Multi-level Taxonomy Review for Sign Language Recognition: Emphasis on Indian Sign Language

Abstract

With the phenomenal increase in image and video databases, there is an increase in the human-computer interaction that recognizes Sign Language. Exchanging information using different gestures between two people is sign language, known as non-verbal communication. Sign language recognition is already done in various languages; however, for Indian Sign Language, there is no adequate amount of work done. This article presents a review on sign language recognition for multiple languages. Data acquisition methods have been over-viewed in four ways (a) Glove-based, (b) Kinect-based, (c) Leap motion controller, and (d) Vision-based. Some of them have pros and cons that have also been discussed for every data acquisition method. Applications of sign language recognition are also discussed. Furthermore, this review also creates a coherent taxonomy to represent the modern research divided into three levels: Level 1 Elementary level (Recognition of sign characters), Level 2 Advanced level (Recognition of sign words), and Level 3 Professional level (Sentence interpretation). The available challenges and issues for each level are also explored in this research to provide valuable perceptions into technological environments. Various publicly available datasets for different sign languages are also discussed. An efficient review of this article shows that the significant exploration of communication via sign acknowledgment has been performed on static, dynamic, isolated, and continuous gestures using various acquisition methods. Comprehensively, the hope is that this study will enable readers to learn new pathways and gain knowledge to carry out further research work in the domain related to sign language recognition.