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http://dx.doi.org/10.9708/jksci.2021.26.12.061

Structuring of Pulmonary Function Test Paper Using Deep Learning  

Jo, Sang-Hyun (Dept. of Computer Science and Engineering, Kangwon National University)
Kim, Dae-Hoon (Dept. of Computer Science and Engineering, Kangwon National University)
Kim, Yoon (Dept. of Computer Science and Engineering, Kangwon National University)
Kwon, Sung-Ok (Dept. of Medical Bigdata Convergence, Kangwon National University)
Kim, Woo-Jin (Dept. of Internal Medicine and Biomedical Informatics, Kangwon National University)
Lee, Sang-Ah (Dept. of Preventive Medicine, Kangwon National University School of Medicine)
Publication Information
Journal of the Korea Society of Computer and Information / v.26, no.12, 2021 , pp. 61-67 More about this Journal
Abstract
In this paper, we propose a method of extracting and recognizing related information for research from images of the unstructured pulmonary function test papers using character detection and recognition techniques. Also, we develop a post-processing method to reduce the character recognition error rate. The proposed structuring method uses a character detection model for the pulmonary function test paper images to detect all characters in the test paper and passes the detected character image through the character recognition model to obtain a string. The obtained string is reviewed for validity using string matching and structuring is completed. We confirm that our proposed structuring system is a more efficient and stable method than the structuring method through manual work of professionals because our system's error rate is within about 1% and the processing speed per pulmonary function test paper is within 2 seconds.
Keywords
Text Detection; Text Recognition; Image Classification; Data Structuring; Deep Learning;
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