Research on Methods to Increase Recognition Rate of Korean Sign Language using Deep LearningResearch on Methods to Increase Recognition Rate of Korean Sign Language using Deep Learning
- Other Titles
- Research on Methods to Increase Recognition Rate of Korean Sign Language using Deep Learning
- Authors
- 권소영; 이용환
- Issue Date
- Feb-2024
- Publisher
- 아이씨티플랫폼학회
- Keywords
- Deep learning; CNN; Sign language; Deaf; Hand detection; Image processing
- Citation
- Journal of Platform Technology, v.12, no.1, pp 3 - 11
- Pages
- 9
- Journal Title
- Journal of Platform Technology
- Volume
- 12
- Number
- 1
- Start Page
- 3
- End Page
- 11
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28538
- ISSN
- 2289-0181
2289-019X
- Abstract
- Deaf people who use sign language as their first language sometimes have difficulty communicating because they do not know spoken Korean. Deaf people are also members of society, so we must support to create a society where everyone can live together. In this paper, we present a method to increase the recognition rate of Korean sign language using a CNN model. When the original image was used as input to the CNN model, the accuracy was 0.96, and when the image corresponding to the skin area in the YCbCr color space was used as input, the accuracy was 0.72. It was confirmed that inserting the original image itself would lead to better results. In other studies, the accuracy of the combined Conv1d and LSTM model was 0.92, and the accuracy of the AlexNet model was 0.92. The CNN model proposed in this paper is 0.96 and is proven to be helpful in recognizing Korean sign language.
- Files in This Item
-
Go to Link
- Appears in
Collections - School of Electronic Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.