Hyojoo Shin
ABSTRACT
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.
- Jong-Jun Park, Chun-Ki Kwon, 2018. Study on Forearm Muscles and Electrode Placements for CNN based Korean Finger Number Gesture Recognition using sEMG Signals, Journal of the Korea Academia-Industrial 19, 8(2018), 260--267Google Scholar
- Boon Giin Lee. 2018. Smart Wearable Hand Device for Sign Language Interpretation System With Sensors Fusion, IEEE Sensors Journal, 18, 3, (2018), 1224--1232Google ScholarCross Ref
- Ki-Sang Kim, Hyung-Il Choi, 2014, Sign Language Recognition System Using SVM and Depth Camera, J. of the Korea Society of Computer and Information, 19, 11, (Nov, 2014), 63--72Google ScholarCross Ref
- G.Anantha Rao, K.syala, P.V.V.Kishore, A.S.C.S.Sastry. 2018. Deep Convolutional Neural Networks for Sign Language Recognition, Proceedings of Conference on Signal Processing and Communication Engineering Systems (March 2018), 194--197Google Scholar
- Ho-Joon Kim, 2012. Two-Stage Neural Networks for Sign Language Pattern Recognition, J. of Korean Institute of Intelligent Systems, 22, 3 (June, 2012), 319--327Google ScholarCross Ref
- Lionel Pigou, Sander Dieleman, Pieter-Jan Kindermans, Benjamin Schrauwen, 2015, Sign Language Recognition Using Convolutional Neural Networks, Proceedings of European Conference on Computer Vision (March 2015) 572--578Google ScholarCross Ref
- N.Priyadharsini, N.Rajeswari, 2017. Sign Language Recognition Using Convolutional Neural Networks, International Journal on Recent and Innovation Trends in Computing and Communication 5,6, (June 2017), 625--628Google Scholar
- Yangho Ji, Sunmok Kim, Ki-Baek Lee. 2017. Sign Language Learning System with Image Sampling and Convolutional Neural Network, Proceedings of IEEE International Conference on Robotic Computing (May 2017), 371--375Google ScholarCross Ref
Index Terms
- Korean sign language recognition based on image and convolution neural network
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