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Reading irregular scene text of arbitrary shape in natural images is still a challenging problem, despite the progress made recently.
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K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., 2016
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A. Gupta, A. Vedaldi, A. Zisserman, Synthetic data for text localisation in natural images, in: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., 2016, pp. 2315–2324
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X. Yang, D. He, Z. Zhou, D. Kifer, C. L. Giles, Learning to read irregular text with attention mechanisms, in: Proc. Int. Joint Conf. Artificial Intell., 2017
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W. Liu, C. Chen, K.-Y. K. Wong, Char-Net: A character-aware neural network for distorted scene text recognition, in: Proc. AAAI Conf. Artificial Intell., 2018
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H. Li, P. Wang, C. Shen, Towards end-to-end text spotting with convolutional recurrent neural networks, in: Proc. IEEE Int. Conf. Comp. Vis., 2017, pp. 5238–5246
2017
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B. Su, S. Lu, Accurate recognition of words in scenes without character segmentation using recurrent neural network, Pattern Recognition 63 (2017) 397–405
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J. Gehring, M. Auli, D. Grangier, D. Yarats, Y. N. Dauphin, Convolutional sequence to sequence learning, in: Proc. Int. Conf. Mach. Learn., 2017
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J. Wang, X. Hu, Gated recurrent convolution neural network for ocr, in: Proc. Adv. Neural Inf. Process. Syst., 2017
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X. Bai, M. Yang, P. Lyu, Y. Xu, J. Luo, Integrating scene text and visual appearance for fine-grained image classification, IEEE Access 6 (2018) 66322–66335
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D. Gurari, Q. Li, A. J. Stangl, A. Guo, C. Lin, K. Grauman, J. Luo, J. P. Bigham, Vizwiz grand challenge: Answering visual questions from blind people, in: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., 2018
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2018
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ICDAR 2019 robust reading challenge on scene text visual question answering, http://rrc.cvc.uab.es/?ch=11 , accessed: 2019-03-09
2019
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M. Liao, J. Zhang, Z. Wan, F. Xie, J. Liang, P. Lyu, C. Yao, X. Bai, Scene text recognition from two-dimensional perspective, in: Proc. AAAI Conf. Artificial Intell., 2019
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H. Li, P. Wang, C. Shen, G. Zhang, Show, attend and read: A simple and strong baseline for irregular text recognition, in: Proc. AAAI Conf. Artificial Intell., 2019
2019
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F. Zhan, S. Lu, ESIR: End-to-end scene text recognition via iterative rectification, in: Proc. IEEE Conf. Comp. Vis. Patt. Recogn., 2019
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M. Dehghani, S. Gouws, O. Vinyals, J. Uszkoreit, Ł. Kaiser, Universal transformers, in: Proc. Int. Conf. Learn. Representations, 2019
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L. J. Luo, Canjie, Z. Sun, MORAN: A multi-object rectified attention network for scene text recognition, in: Pattern Recogn., 2019
2019
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J. Baek, G. Kim, J. Lee, S. Park, D. Han, S. Yun, S. J. Oh, H. Lee, What is wrong with scene text recognition model comparisons? dataset and model analysis (2019) 4715–4723
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M. Yang, Y. Guan, M. Liao, X. He, K. Bian, S. Bai, C. Yao, X. Bai, Symmetry-constrained rectification network for scene text recognition, in: Proc. IEEE Int. Conf. Comp. Vis., 2019
2019
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M. Liao, P. Lyu, M. He, C. Yao, X. Bai, Mask textspotter: An end-to-end trainable neural network for spotting text with arbitrary shapes, IEEE Transactions on Pattern Analysis and Machine Intelligence PP (99) (2019) 1–1
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M. Jaderberg, K. Simonyan, A. Zisserman, et al., Spatial transformer networks, in: Proc. Adv. Neural Inf. Process. Syst., 2015, pp. 2017–2025
2025
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