In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) has been redirecting to new domain since Jan. 1st, 2025.
See More
App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 8

Deep learning-based sign language translation: past, present, and future

Zhang Lei1,2,3
Wang Zhenyu1,2,3
Lian Shuaishuai3,4
Pu Bingqian1,2,3
Liu Yutao1,2,3
Qin Mingzhe3,4
1. School of Information & Electronic Technology, Jiamusi University, Jiamusi Heilongjiang 154000, China
2. Heilongjiang Province Key Laboratory of Autonomous Intelligence & Information Processing, School of Information & Electronic Technology, Jiamusi University, Jiamusi Heilongjiang 154007, China
3. Jiamusi Key Laboratory of Satellite Navigation Technology & Equipment Engineering Technology, Jiamusi Heilongjiang 154007, China
4. Handan Vocational College of Sience & Technology, Handan 056046, China
DOI: 10.19734/j.issn.1001-3695.2025.01.0001

Abstract

Sign Language Translation (SLT) based on deep learning aims to translate sign language gestures into natural language using deep learning techniques to improve translation accuracy. SLT reduces communication barriers between hearing individuals and those with hearing impairments. However, SLT faces numerous challenges due to the lack of standardization across different sign languages and the mismatch between sign language gestures and spoken language sentence structures. With the development of deep learning technologies, SLT has gained widespread attention from researchers. This paper summarizes recent approaches to SLT based on deep learning and classifies them into four categories according to model structure and development history: linear structure-based SLT, encoder-decoder architecture-based SLT, large model fine-tuning-based SLT, and contrastive learning-based SLT. By analyzing the characteristics and performance of these methods, this study provides a comprehensive evaluation of the progress in SLT methods. Finally, the paper outlines future research directions, focusing on the potential and development trends of key technologies, including real-time translation, contrastive learning-based SLT, and large model fine-tuning-based SLT.

Foundation Support

黑龙江省自然科学基金联合基金培育项目(PL2024F002)
黑龙江省省属高等学校基本科研业务费优秀创新团队建设项目(2022-KYYWF-0654)
佳木斯大学国家基金培育项目(JMSUGPZR2022-014)
黑龙江省自主智能与信息处理重点实验室开放课题(ZZXC202302)
佳木斯大学"东极"学术团队(团队编号:DJXSTD202417)
黑龙江省省属本科高校优秀青年教师基础研究支持计划(YQJH2024239)
黑龙江省外国专家项目(G2024020)
佳木斯大学博士专项科研启动项目(项目编号:JMSUBZ2024-07)
大学生创新创业计划(S202310222013)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.01.0001
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 8

Publish History

[2025-03-20] Accepted Paper

Cite This Article

张磊, 王振宇, 连帅帅, 等. 基于深度学习的手语翻译：过去、现状与未来 [J]. 计算机应用研究, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2025.01.0001. (Zhang Lei, Wang Zhenyu, Lian Shuaishuai, et al. Deep learning-based sign language translation: past, present, and future [J]. Application Research of Computers, 2025, 42 (8). (2025-04-17). https://doi.org/10.19734/j.issn.1001-3695.2025.01.0001. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)