Scene text image super-resolution via textual reasoning and multiscale cross-convolution
Author:
Funder
National Natural Science Foundation of China
Joint Funds of Natural Science Foundation of Shandong Province
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-023-05251-7.pdf
Reference36 articles.
1. Qiao Z, Zhou Y, Yang D et al. (2020) Seed: semantics enhanced encoder-decoder framework for scene text recognition. 2020 IEEE/CVF Conference on computer vision and pattern recognition (CVPR) pp 13525–13534. https://doi.org/10.1109/cvpr42600.2020.01354
2. Aberdam A, Litman R, Tsiper S et al. (2020) Sequence-to-sequence contrastive learning for text recognition. 2021 IEEE/CVF Conference on computer vision and pattern recognition (CVPR) pp 15297–15307. https://doi.org/10.1109/CVPR46437.2021.01505
3. Yue X, Kuang Z, Lin C et al (2020) Robustscanner: dynamically enhancing positional clues for robust text recognition. In: European conference on computer vision, https://doi.org/10.1007/978-3-030-58529-7_9
4. Wang Y, Xie H, Fang S et al (2022) Petr: rethinking the capability of transformer-based language model in scene text recognition. IEEE Trans Image Process 31:5585–5598. https://doi.org/10.1109/TIP.2022.3197981
5. Dong C, Loy CC, He K et al (2014) Image super-resolution using deep convolutional networks. IEEE Trans Patt Anal Mach Intell 38:295–307. https://doi.org/10.1109/TPAMI.2015.2439281
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