Intelligent Recognition of Time Stamp Characters in Solar Scanned Images from Film

IF 1.6 4区 物理与天体物理 Q3 ASTRONOMY & ASTROPHYSICS Advances in Astronomy Pub Date : 2019-08-28 DOI:10.1155/2019/6565379
Jiafeng Zhang, Guangzhong Lin, S. Zeng, S. Zheng, Xiao Yang, Gang-Hua Lin, X. Zeng, Haimin Wang
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引用次数: 2

Abstract

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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胶片太阳扫描图像时间戳特征的智能识别
在数码相机出现之前,太阳观测图像通常记录在胶片上,而日期和时间等信息则被印在胶片上的同一帧上。为了有效地利用图像数据,对胶片上的时间戳信息进行提取具有重要意义。本文介绍了一种提取时间戳信息的智能方法——卷积神经网络(CNN),它是一种多层神经网络结构的深度学习算法,可以识别扫描太阳图像中的时间戳特征。对1963 ~ 2003年国家太阳观测台的数字化数据进行了时间戳解码。实验结果表明,该方法具有准确、快速的特点。我们完成了700多万张图像的时间戳信息提取,准确率达到98%。
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来源期刊
Advances in Astronomy
Advances in Astronomy ASTRONOMY & ASTROPHYSICS-
CiteScore
2.70
自引率
7.10%
发文量
10
审稿时长
22 weeks
期刊介绍: Advances in Astronomy publishes articles in all areas of astronomy, astrophysics, and cosmology. The journal accepts both observational and theoretical investigations into celestial objects and the wider universe, as well as the reports of new methods and instrumentation for their study.
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