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http://dx.doi.org/10.6109/jkiice.2016.20.9.1657

Streamlined GoogLeNet Algorithm Based on CNN for Korean Character Recognition  

Kim, Yeon-gyu (Department of Computer Science Engineering, Pusan National University)
Cha, Eui-young (Department of Computer Science Engineering, Pusan National University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.20, no.9, 2016 , pp. 1657-1665 More about this Journal
Abstract
Various fields are being researched through Deep Learning using CNN(Convolutional Neural Network) and these researches show excellent performance in the image recognition. In this paper, we provide streamlined GoogLeNet of CNN architecture that is capable of learning a large-scale Korean character database. The experimental data used in this paper is PHD08 that is the large-scale of Korean character database. PHD08 has 2,187 samples for each character and there are 2,350 Korean characters that make total 5,139,450 sample data. As a training result, streamlined GoogLeNet showed over 99% of test accuracy at PHD08. Also, we made additional Korean character data that have fonts that are not in the PHD08 in order to ensure objectivity and we compared the performance of classification between streamlined GoogLeNet and other OCR programs. While other OCR programs showed a classification success rate of 66.95% to 83.16%, streamlined GoogLeNet showed 89.14% of the classification success rate that is higher than other OCR program's rate.
Keywords
Classification; Convolutional Neural Network; Deep Learning; Korean Character Recognition;
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Times Cited By KSCI : 1  (Citation Analysis)
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