Text Image Super Resolution Using Deep Attention Neural Network
Yun Liu, Remina Yano, Hiroshi Watanabe, Takuya Suzuki, Takeshi Chujoh, Tomohiro Ikai · 2021 IEEE 10th Global Conference on Consumer Electronics (GCCE) · 2021
In this paper, we propose a super-resolution method for text images to improve the accuracy of optical character recognition (OCR). The accuracy of OCR is closely related to the resolution of the image, and when OCR is applied to low resolution text images, satisfactory results are often not obtained. In the proposed method, we extract more representative feature information from text images by combining channel and spatial attention. Furthermore, we propose a new loss function called “edge loss”. Experimental results show that the recognition accuracy of text images by our SR method is 5.87% higher than that of the original low-resolution images, and also higher than the results of BICUBIC and the baseline model.