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JBE, vol. 28, no. 2, pp.178-184, March, 2023

DOI: https://doi.org/10.5909/JBE.2023.28.2.178

Korean Text Image Super-Resolution for Improving Text Recognition

Junhyeong Kwon and Nam Ik Cho

C.A E-mail: nicho@snu.ac.kr

Abstract:
Finding texts in general scene images and recognizing their contents is a very important task that can be used as a basis for
robot vision, visual assistance, and so on. However, for the low-resolution text images, the degradations, such as noise or blur
included in text images, are more noticeable, which leads to severe performance degradation of text recognition accuracy. In this
paper, we propose a new Korean text image super-resolution based on a Transformer-based model, which generally shows higher
performance than convolutional neural networks. In the experiments, we show that text recognition accuracy for Korean text images
can be improved when our proposed text image super-resolution method is used. We also propose a new Korean text image
dataset for training our model, which contains massive HR-LR Korean text image pairs.


Keyword: Scene Text Image Super-Resolution, Korean Text, Transformer

Reference:
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