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

TL;DR

This paper presents a CNN-based method for accurately and efficiently extracting time stamp characters from scanned solar images on film, enabling better utilization of historical solar observational data.

Contribution

The paper introduces a deep learning approach using CNNs to automatically recognize time stamps in solar images, achieving high accuracy on a large dataset from 1963 to 2003.

Findings

Achieved 98% accuracy in time stamp recognition.

Processed over 7 million images efficiently.

Demonstrated the method's effectiveness for historical solar data.

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\%.