| Title |
A study on status information extraction of electrical installations through image super-resolution based on ESRGAN |
| Authors |
문재현(Jae-Hyeon Mun) ; 이기연(Ki-Yeon Lee) ; 채동주(Dong-Ju Chae) ; 임승택(Seung-Taek Lim) ; 송현제(Hyun-Je Song) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.10.1497 |
| Keywords |
Image super-resolution; Enhanced SRGAN; ESRGAN; Display information extraction; Optical character recognition |
| Abstract |
Electrical equipment performs external communication for monitoring. When a communication malfunction occurs, there is a need for a way to extract information from the electrical equipment. One approach is to capture the display of electrical equipment with an image using devices like CCTV and then extract the information from the image using optical character recognition. However, the images are low-resolution, so the optical character recognition does not work well on the image. This paper proposes a simple method to improve the performance of optical character recognition with a super-resolution model. The proposed method converts the low-resolution image to a high-resolution image through the super-resolution model trained with a proper electrical equipment image dataset. As a result, optical character recognition can extract information from high-resolution images. Experiments on a real-world electrical equipment image dataset show that the proposed method helps to extract information from electrical equipment images |