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Sunspot drawings handwritten character recognition method based on deep learning
Zheng, Sheng1; Zeng, Xiangyun1; Lin, Ganghua2; Zhao, Cui2; Feng YL(冯永利)3; Tao JP(陶金萍)3; Zhu, Daoyuan1; Xiong, Li1
Source PublicationNEW ASTRONOMY
2016-05-01
Volume45Pages:54-59
DOI10.1016/j.newast.2015.11.001
Contribution Rank第3完成单位
Indexed BySCI
KeywordSunspot Drawings Convolution Neural Network Handwriting Character Recognition
Abstract

High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate. (C) 2015 Elsevier B.V. All rights reserved.

Funding ProjectNational Natural Science Fund Committee ; Chinese Academy of Sciences astronomical union funds[U1331113] ; Chinese Academy of Sciences astronomical union funds[2014FY120300]
Funding OrganizationNational Natural Science Fund Committee ; Chinese Academy of Sciences astronomical union funds[U1331113, 2014FY120300]
Language英语
Subject Area天文学 ; 太阳与太阳系
MOST Discipline Catalogue理学 ; 理学::天文学
SubtypeArticle
PublisherELSEVIER SCIENCE BV
Publication PlacePO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS
ISSN1384-1076
URL查看原文
WOS IDWOS:000369200700009
WOS Research AreaAstronomy & Astrophysics
WOS SubjectAstronomy & Astrophysics
WOS KeywordCATALOG
Citation statistics
Cited Times:3[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ynao.ac.cn/handle/114a53/9325
Collection太阳物理研究组
Corresponding AuthorZeng, Xiangyun
Affiliation1.College of Science, China Three Gorges University, Yichang 443002, China
2.Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China
3.Yunnan Observatories, Chinese Academy of Sciences, P.O. Box 110, Kunming, Yunnan 650011, China
Recommended Citation
GB/T 7714
Zheng, Sheng,Zeng, Xiangyun,Lin, Ganghua,et al. Sunspot drawings handwritten character recognition method based on deep learning[J]. NEW ASTRONOMY,2016,45:54-59.
APA Zheng, Sheng.,Zeng, Xiangyun.,Lin, Ganghua.,Zhao, Cui.,Feng YL.,...&Xiong, Li.(2016).Sunspot drawings handwritten character recognition method based on deep learning.NEW ASTRONOMY,45,54-59.
MLA Zheng, Sheng,et al."Sunspot drawings handwritten character recognition method based on deep learning".NEW ASTRONOMY 45(2016):54-59.
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