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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국정보처리학회 JIPS(Journal of Information Processing Systems) JIPS(Journal of Information Processing Systems) 제14권 제1호
- 발행연도
- 2018.1
- 수록면
- 205 - 217 (13page)
이용수
초록· 키워드
오류제보하기
Deep learning using convolutional neural networks (CNNs) is being studied in various fields of imagerecognition and these studies show excellent performance. In this paper, we compare the performance ofCNN architectures, KCR-AlexNet and KCR-GoogLeNet. The experimental data used in this paper is obtainedfrom PHD08, a large-scale Korean character database. It has 2,187 samples of each Korean character with2,350 Korean character classes for a total of 5,139,450 data samples. In the training results, KCR-AlexNetshowed an accuracy of over 98% for the top-1 test and KCR-GoogLeNet showed an accuracy of over 99% forthe top-1 test after the final training iteration. We made an additional Korean character dataset with fonts thatwere not in PHD08 to compare the classification success rate with commercial optical character recognition(OCR) programs and ensure the objectivity of the experiment. While the commercial OCR programs showed66.95% to 83.16% classification success rates, KCR-AlexNet and KCR-GoogLeNet showed averageclassification success rates of 90.12% and 89.14%, respectively, which are higher than the commercial OCRprograms’ rates. Considering the time factor, KCR-AlexNet was faster than KCR-GoogLeNet when they weretrained using PHD08; otherwise, KCR-GoogLeNet had a faster classification speed.
목차
등록된 정보가 없습니다.
참고문헌 (26)
참고문헌 신청
D. Cireşan / 2012 / Multi-column deep neural network for traffic sign classification / Neural Networks 32:333~338
N. Kalchbrenner / A convolutional neural network for modelling sentences
P. L. Callet / 2006 / A convolutional neural network approach for objective video quality assessment / IEEE Transactions on Neural Networks 17(5):1316~1327
D. C. Ciresan / 2011 / Convolutional neural network committees for handwritten character classification / Proceedings of the International Conference on Document Analysis and Recognition (ICDAR) :1135~1139
T. Wang / 2012 / End-to-end text recognition with convolutional neural networks / Proceedings of the 21st International Conference on Pattern Recognition(ICPR 2012) :3304~3308
N. Kalchbrenner / A convolutional neural network for modelling sentences
P. L. Callet / 2006 / A convolutional neural network approach for objective video quality assessment / IEEE Transactions on Neural Networks 17(5):1316~1327
D. C. Ciresan / 2011 / Convolutional neural network committees for handwritten character classification / Proceedings of the International Conference on Document Analysis and Recognition (ICDAR) :1135~1139
T. Wang / 2012 / End-to-end text recognition with convolutional neural networks / Proceedings of the 21st International Conference on Pattern Recognition(ICPR 2012) :3304~3308
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