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Technology of Graphic & Image
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1589-1594,1600

Chinese sign language translation method fusing adaptive graph convolution and Transformer sequence model

Ying Jie1
Xu Wencheng1
Yang Haima1
Liu Jin2
Zheng Leqian1
1. School of Optoelectronic Information & Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China
2. School of Electronic & Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China

Abstract

In order to solve the problems of feature extraction and temporal translation in sign language translation methods, this paper proposed an AGCN-T sign language translation network that combined AGCN and Transformer temporal model. The AGCN module learnt the interactive spatial dependency information of skeleton nodes in sign language actions. The Transformer temporal module captured the time relationship feature information of the sign language action sequence and translated it into understandable sign language semantic information. In addition, in the preprocessing part, this paper proposed a key frame extraction algorithm for moving windows, and used MediaPipe pose estimation algorithm to extract the skeleton of key frame image sequences. The experimental results show that the proposed method achieves a word error rate of 3.75% and an accuracy of 97.87% in the large-scale Chinese continuous sign language dataset CCSL, which is superior to other advanced sign language translation methods.

Foundation Support

国家自然科学基金资助项目(62172280)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.08.0463
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 5
Section: Technology of Graphic & Image
Pages: 1589-1594,1600
Serial Number: 1001-3695(2023)05-048-1589-06

Publish History

[2022-11-17] Accepted Paper
[2023-05-05] Printed Article

Cite This Article

应捷, 徐文成, 杨海马, 等. 融合自适应图卷积与Transformer序列模型的中文手语翻译方法 [J]. 计算机应用研究, 2023, 40 (5): 1589-1594,1600. (Ying Jie, Xu Wencheng, Yang Haima, et al. Chinese sign language translation method fusing adaptive graph convolution and Transformer sequence model [J]. Application Research of Computers, 2023, 40 (5): 1589-1594,1600. )

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.


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