TY - JOUR
T1 - Intelligent recognition of time stamp characters in solar scanned images from film
AU - Zhang, Jiafeng
AU - Lin, Guangzhong
AU - Zeng, Shuguang
AU - Zheng, Sheng
AU - Yang, Xiao
AU - Lin, Ganghua
AU - Zeng, Xiangyun
AU - Wang, Haimin
N1 - Funding Information:
This work is supported in part by the National Natural Science Foundation of China under Grants U1731124, U1531247, 11427803, 11427901, and 11873062, the 13th Five-year Informatization Plan of Chinese Academy of Sciences under Grant XXH13505-04, and the Beijing Municipal Science and Technology Project under Grant Z181100002918004. Haimin Wang acknowledges the support of US NSF under grant AGS-1620875. The authors are grateful to the National Solar Observatory for providing the original film data.*%blankline%*
Funding Information:
https://orcid.org/0000-0002-3883-548X Zhang Jiafeng jfzhang666@126.com 1 Lin Guangzhong 2016146218@ctgu.edu.cn 1 https://orcid.org/0000-0002-3216-7476 Zeng Shuguang zengshuguang19@163.com 1 Zheng Sheng zsh@ctgu.edu.cn 1 Yang Xiao yangx@nao.cas.cn 2 Lin Ganghua lgh@nao.cas.cn 2 Zeng Xiangyun 13207224039@163.com 1 https://orcid.org/0000-0002-5233-565X Wang Haimin haimin.wang@njit.edu 3 Kovacs Geza 1 College of Science China Three Gorges University Yichang 443002 China ctgu.edu.cn 2 Key Laboratory of Solar Activity National Astronomical Observatories Chinese Academy of Sciences Beijing 100101 China cas.cn 3 Institute for Space Weather Sciences New Jersey Institute of Technology 323 Martin Luther King Boulevard Newark NJ 07102-1982 USA njit.edu 2019 28 8 2019 2019 11 04 2019 04 07 2019 18 07 2019 28 8 2019 2019 Copyright © 2019 Jiafeng Zhang et al. This is an open access article distributed under the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Prior to the availability of digital cameras, the solar observational images are typically recorded on films, and the information such as date and time were stamped in the same frames on film. It is significant to extract the time stamp information on the film so that the researchers can efficiently use the image data. This paper introduces an intelligent method for extracting time stamp information, namely, the convolutional neural network (CNN), which is an algorithm in deep learning of multilayer neural network structures and can identify time stamp character in the scanned solar images. We carry out the time stamp decoding for the digitized data from the National Solar Observatory from 1963 to 2003. The experimental results show that the method is accurate and quick for this application. We finish the time stamp information extraction for more than 7 million images with the accuracy of 98%. National Natural Science Foundation of China U1731124 U1531247 11427803 11427901 11873062 Chinese Academy of Sciences XXH13505-04 Beijing Municipal Science and Technology Project Z181100002918004 National Science Foundation AGS-1620875
Publisher Copyright:
© 2019 Jiafeng Zhang et al.
PY - 2019
Y1 - 2019
N2 - Prior to the availability of digital cameras, the solar observational images are typically recorded on films, and the information such as date and time were stamped in the same frames on film. It is significant to extract the time stamp information on the film so that the researchers can efficiently use the image data. This paper introduces an intelligent method for extracting time stamp information, namely, the convolutional neural network (CNN), which is an algorithm in deep learning of multilayer neural network structures and can identify time stamp character in the scanned solar images. We carry out the time stamp decoding for the digitized data from the National Solar Observatory from 1963 to 2003. The experimental results show that the method is accurate and quick for this application. We finish the time stamp information extraction for more than 7 million images with the accuracy of 98%.
AB - Prior to the availability of digital cameras, the solar observational images are typically recorded on films, and the information such as date and time were stamped in the same frames on film. It is significant to extract the time stamp information on the film so that the researchers can efficiently use the image data. This paper introduces an intelligent method for extracting time stamp information, namely, the convolutional neural network (CNN), which is an algorithm in deep learning of multilayer neural network structures and can identify time stamp character in the scanned solar images. We carry out the time stamp decoding for the digitized data from the National Solar Observatory from 1963 to 2003. The experimental results show that the method is accurate and quick for this application. We finish the time stamp information extraction for more than 7 million images with the accuracy of 98%.
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U2 - 10.1155/2019/6565379
DO - 10.1155/2019/6565379
M3 - Article
AN - SCOPUS:85072375557
SN - 1687-7969
VL - 2019
JO - Advances in Astronomy
JF - Advances in Astronomy
M1 - 6565379
ER -