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

• Main Authors: Jiafeng Zhang, Guangzhong Lin, Shuguang Zeng, Sheng Zheng, Xiao Yang, Ganghua Lin, Xiangyun Zeng, Haimin Wang
• Format: Article
• Language: English
• Published: Hindawi Limited 2019-01-01
• Series: Advances in Astronomy
• Online Access: http://dx.doi.org/10.1155/2019/6565379

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%.