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 - 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%.
UR - http://www.scopus.com/inward/record.url?scp=85072375557&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85072375557&partnerID=8YFLogxK
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 -