State of the Art of Automation in Sign Language: A Systematic Review

Author:

Kumar Attar Rakesh1ORCID,Goyal Vishal2ORCID,Goyal Lalit3ORCID

Affiliation:

1. Department of Computer Science, University College Miranpur, Patiala, Punjab, India

2. Department of Computer Science, Punjabi University, Patiala, Punjab, India

3. Department of Computer Science, DAV College, Jalandhar, Punjab, India

Abstract

Sign language is the fundamental communication language of deaf people. Efforts to develop sign language generation systems can make the life of these people smooth and effortless. Despite the importance of sign language generation systems, there is a paucity of a systematic literature review. This is the foremost recognizable scholastic literature review of sign language generation systems. It presents a scholastic database of the literature between 1998 and 2020 and suggests classification criteria to systematize research studies. Four hundred fourteen research studies were recognized and reviewed for their direct pertinence to sign language generation systems. One hundred sixty-two research studies were subsequently chosen, examined, and classified. Each of the 162 chosen research papers was categorized based on 30 sign languages and was further comparatively analyzed based on seven comparison parameters (input form, translation technologies, application domain, use of parsers/grammars, manual/non-manual features, accuracy, and output form). It is evident from our research findings that the majority of research on sign language generation was carried out using data-driven approaches in the absence of proper grammar rules and generated only manual signs. This research study may provide researchers a roadmap toward future research directions and facilitate the compilation of information in the field of sign language generation.

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Reference215 articles.

1. Design and development of a frame based MT system for English-to-ISL

2. Indo-Pakistani Sign Language Grammar: A Typological Outline

3. World Health Organization. 2022. Retrieved March 1 2022 from https://www.who.int/en/news-room/fact-sheets/detail/deafness-and-hearing-loss.

4. National Center for Health Statistics. 2022. Retrieved March 1 2022 from https://www.startasl.com/american-sign-language.

5. BDA: British Deaf Association BSL Statistics. 2022. Retrieved March 1 2022 from https://bda.org.uk/help-resources/#statistics.

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Efhamni: A Deep Learning-Based Saudi Sign Language Recognition Application;Sensors;2024-05-14

2. Cognitive Classifier of Hand Gesture Images for Automated Sign Language Recognition: Soft Robot Assistance Based on Neutrosophic Markov Chain Paradigm;Computers;2024-04-22

3. Hand Gesture Recognition: A Contemporary Overview of Techniques;2024 International Conference on Automation and Computation (AUTOCOM);2024-03-14

4. Exploring Sign Language Detection on Smartphones: A Systematic Review of Machine and Deep Learning Approaches;Advances in Human-Computer Interaction;2024-03-11

5. Comprehensive study of Sign Language Conversion Using Machine Learning;2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON);2023-12-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3