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Sunspot drawings handwritten character recognition method based on deep learning
Affiliation:1. 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;1. Department of Physics and Astronomy, Butler University, Indianapolis, IN 46208, USA;2. College of Science/Department of Physics & NAOC-GZU-Sponsored Center for Astronomy Research, Guizhou University, Guiyang 550025, PR China;3. Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, Kunming 650011, PR China;1. School of Studies in Physics, Jiwaji University, Gwalior 474011, India;2. Greater Noida Institute of Technology, Plot No. 7, Knowledge Park-II, Greater Noida 201306, India;3. Department of Physics, Lovely Professional University, Phagwara 144411, India;4. Zentrum für Astronomie und Astrophysik, Technische Universität Berlin, Hardenbergstrasse 36, D-10623 Berlin, Germany
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.
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