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ASL Recognition by the Layered Learning Model Using Clustered Groups

Jungsoo Shin, Jaehee Jung*

Department of Information and Communication Engineering, Myongji University, Yongin, Korea

* Corresponding Author: Jaehee Jung. Email: email

Computer Systems Science and Engineering 2023, 45(1), 51-68.


American Sign Language (ASL) images can be used as a communication tool by determining numbers and letters using the shape of the fingers. Particularly, ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons, because sign language is their only channel of expression. Representative ASL recognition methods primarily adopt images, sensors, and pose-based recognition techniques, and employ various gestures together with hand-shapes. This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers. In the proposed model, the collected ASL images were clustered based on similarities in shape, and clustered group classification was first performed, followed by reclassification within the group. The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition. After selecting the optimized group, we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing. The proposed model exhibited an improved performance compared with the general classification model.


Cite This Article

APA Style
Shin, J., Jung, J. (2023). ASL recognition by the layered learning model using clustered groups. Computer Systems Science and Engineering, 45(1), 51-68.
Vancouver Style
Shin J, Jung J. ASL recognition by the layered learning model using clustered groups. Comput Syst Sci Eng. 2023;45(1):51-68
IEEE Style
J. Shin and J. Jung, "ASL Recognition by the Layered Learning Model Using Clustered Groups," Comput. Syst. Sci. Eng., vol. 45, no. 1, pp. 51-68. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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