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Structuring of Pulmonary Function Test Paper Using Deep Learning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2021, 26(12), pp.61-67
  • DOI : 10.9708/jksci.2021.26.12.061
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : November 4, 2021
  • Accepted : November 22, 2021
  • Published : December 31, 2021

Sang-Hyun Jo 1 Dae-Hoon Kim 1 Kim, yoon 1 Song Ok Kwon 1 Kim, WooJin 1 Sang-Ah Lee 1

1강원대학교

Accredited

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.

Citation status

* References for papers published after 2023 are currently being built.

This paper was written with support from the National Research Foundation of Korea.