TY - GEN
T1 - Korean sign language recognition based on image and convolution neural network
AU - Shin, Hyojoo
AU - Kim, Woo Je
AU - Jang, Kyoung Ae
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019
Y1 - 2019
N2 - The purpose of this paper is to develop a convolution neural network based model for Korean sign language recognition. For this purpose, sign language videos were collected for 10 selected words of Korean sign language and these videos were converted into images to have 9 frames. The images with 9 frames were used as input data for the convolution neural network based model developed in this study. In order to develop the model for Korean sign language recognition, experiments for determining the number of convolution layers was first performed. Second, experiments for the pooling which intentionally reduces the features of the feature map was performed. Third, we conducted an experiment to reduce over fitting in the model learning process. Based on the experiments, we have developed a convolution neural network based model for Korean sign language recognition. The accuracy of the developed model was about 84.5% for the 10 selected Korean sign words.
AB - The purpose of this paper is to develop a convolution neural network based model for Korean sign language recognition. For this purpose, sign language videos were collected for 10 selected words of Korean sign language and these videos were converted into images to have 9 frames. The images with 9 frames were used as input data for the convolution neural network based model developed in this study. In order to develop the model for Korean sign language recognition, experiments for determining the number of convolution layers was first performed. Second, experiments for the pooling which intentionally reduces the features of the feature map was performed. Third, we conducted an experiment to reduce over fitting in the model learning process. Based on the experiments, we have developed a convolution neural network based model for Korean sign language recognition. The accuracy of the developed model was about 84.5% for the 10 selected Korean sign words.
KW - Convolution Neural Network
KW - Image
KW - Korean Sign Language
KW - Recognition
UR - http://www.scopus.com/inward/record.url?scp=85065781677&partnerID=8YFLogxK
U2 - 10.1145/3313950.3313967
DO - 10.1145/3313950.3313967
M3 - Conference contribution
AN - SCOPUS:85065781677
SN - 9781450360920
T3 - ACM International Conference Proceeding Series
SP - 52
EP - 55
BT - ACM International Conference Proceeding Series
PB - Association for Computing Machinery
T2 - 2nd International Conference on Image and Graphics Processing, ICIGP 2019
Y2 - 23 February 2019 through 25 February 2019
ER -