Abstract
To generate high-resolution text images from available low-resolution ones is of great value to many text-related applications, especially text recognition. In this paper, we propose an effective super-resolution method for text images based on Conditional Generative Adversarial Network (cGAN). Specifically, we improve the cGAN model by removing the Batch Normalization layers and introducing the Inception structure to make it more suited to the text image super-resolution task, which contribute to the overall enhanced performances of the proposed method relative to the original cGAN model. Experiment results on public dataset demonstrate the effectiveness of the proposed method.
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Acknowledgments
Research supported by the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20171345 and the National Natural Science Foundation of China under Grant Nos. 61003113, 61321491, 61672273.
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Wang, Y., Ding, W., Su, F. (2018). Super-Resolution of Text Image Based on Conditional Generative Adversarial Network. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11166. Springer, Cham. https://doi.org/10.1007/978-3-030-00764-5_25
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