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End-to-end scene text spotting has made significant progress due to its intrinsic synergy between text detection and recognition.
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2017
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2017
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P. Lyu, M. Liao, C. Yao, W. Wu, and X. Bai, “Mask TextSpotter: An end-to-end trainable neural network for spotting text with arbitrary shapes,” in Proc. Eur. Conf. Comp. Vis
2018
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S. Long, J. Ruan, W. Zhang, X. He, W. Wu, and C. Yao, “TextSnake: A flexible representation for detecting text of arbitrary shapes,” in Proc. Eur. Conf. Comp. Vis
2018
Cited alongside, same era.
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2018
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L. Qiao, S. Tang, Z. Cheng, Y. Xu, Y. Niu, S. Pu, and F. Wu, “Text perceptron: Towards end-to-end arbitrary-shaped text spotting,” in Proc. AAAI Conf. Artificial Intell
2020
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M. Liao, B. Shi, and X. Bai, “TextBoxes++: A single-shot oriented scene text detector,” IEEE Trans. Image Process
2018
Cited alongside, same era.
X. Liu, D. Liang, S. Yan, D. Chen, Y. Qiao, and J. Yan, “FOTS: Fast oriented text spotting with a unified network,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2018
Cited alongside, same era.
T. He, Z. Tian, W. Huang, C. Shen, Y. Qiao, and C. Sun, “An end-to-end textspotter with explicit alignment and attention,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2018
Cited alongside, same era.
Y. Sun, C. Zhang, Z. Huang, J. Liu, J. Han, and E. Ding, “TextNet: Irregular text reading from images with an end-to-end trainable network,” in Proc. Asian Conf. Comp. Vis
2018
Cited alongside, same era.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” Proc. Int. Conf. Learn. Representations
2018
Cited alongside, same era.
W. Feng, W. He, F. Yin, X.-Y. Zhang, and C.-L. Liu, “TextDragon: An end-to-end framework for arbitrary shaped text spotting,” in Proc. IEEE Int. Conf. Comp. Vis
2019
Cited alongside, same era.
M. Liao, P. Lyu, M. He, C. Yao, W. Wu, and X. Bai, “Mask TextSpotter: An end-to-end trainable neural network for spotting text with arbitrary shapes,” IEEE Trans. Pattern Anal. Mach. Intell
2019
Cited alongside, same era.
2021
Later among the works it cites.
L. Qiao, Y. Chen, Z. Cheng, Y. Xu, Y. Niu, S. Pu, and F. Wu, “MANGO: A mask attention guided one-stage scene text spotter,” in Proc. AAAI Conf. Artificial Intell
2021
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X. Zhu, W. Su, L. Lu, B. Li, X. Wang, and J. Dai, “Deformable DETR: Deformable transformers for end-to-end object detection,” Proc. Int. Conf. Learn. Representations
2021
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S. Fang, H. Xie, Y. Wang, Z. Mao, and Y. Zhang, “Read like humans: Autonomous, bidirectional and iterative language modeling for scene text recognition,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2021
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D. Meng, X. Chen, Z. Fan, G. Zeng, H. Li, Y. Yuan, L. Sun, and J. Wang, “Conditional DETR for fast training convergence,” in Proc. IEEE Int. Conf. Computer Vision
2021
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P. Wang, C. Zhang, F. Qi, S. Liu, X. Zhang, P. Lyu, J. Han, J. Liu, E. Ding, and G. Shi, “PGNet: Real-time arbitrarily-shaped text spotting with point gathering network,” in Proc. AAAI Conf. Artificial Intell
2021
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2022
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T. Chen, S. Saxena, L. Li, D. J. Fleet, and G. Hinton, “Pix2Seq: A language modeling framework for object detection,” in Proc. Int. Conf. Learn. Representations
2022
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J. Fan, Z. Zhang, and T. Tan, “Pointly-supervised panoptic segmentation,” in Proc. Eur. Conf. Comp. Vis
2022
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J. Tang, S. Qiao, B. Cui, Y. Ma, S. Zhang, and D. Kanoulas, “You can even annotate text with voice: Transcription-only-supervised text spotting,” in Proc. ACM Int. Conf. Multimedia
2022
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P. Wang, H. Li, and C. Shen, “Towards end-to-end text spotting in natural scenes,” IEEE Trans. Pattern Anal. Mach. Intell
2022
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W. Wang, E. Xie, X. Li, X. Liu, D. Liang, Y. Zhibo, T. Lu, and C. Shen, “PAN++: Towards efficient and accurate end-to-end spotting of arbitrarily-shaped text,” IEEE Trans. Pattern Anal. Mach. Intell
2022
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Y. Liu, C. Shen, L. Jin, T. He, P. Chen, C. Liu, and H. Chen, “ABCNet v2: Adaptive bezier-curve network for real-time end-to-end text spotting,” IEEE Trans. Pattern Anal. Mach. Intell
2022
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M. Huang, Y. Liu, Z. Peng, C. Liu, D. Lin, S. Zhu, N. Yuan, K. Ding, and L. Jin, “SwinTextSpotter: Scene text spotting via better synergy between text detection and text recognition,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2022
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X. Zhang, Y. Su, S. Tripathi, and Z. Tu, “Text spotting transformers,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2022
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S. Fang, Z. Mao, H. Xie, Y. Wang, C. Yan, and Y. Zhang, “ABINet++: Autonomous, bidirectional and iterative language modeling for scene text spotting,” IEEE Trans. Pattern Anal. Mach. Intell
2022
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Y. Kittenplon, I. Lavi, S. Fogel, Y. Bar, R. Manmatha, and P. Perona, “Towards weakly-supervised text spotting using a multi-task transformer,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2022
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S. Liu, F. Li, H. Zhang, X. Yang, X. Qi, H. Su, J. Zhu, and L. Zhang, “DAB-DETR: Dynamic anchor boxes are better queries for DETR,” in Int. Conf. Learning Representations
2022
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R. Ronen, S. Tsiper, O. Anschel, I. Lavi, A. Markovitz, and R. Manmatha, “GLASS: Global to local attention for scene-text spotting,” in Proc. Eur. Conf. Comp. Vis
2022
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P. Lu, H. Wang, S. Zhu, J. Wang, X. Bai, and W. Liu, “Boundary TextSpotter: Toward arbitrary-shaped scene text spotting,” IEEE Transactions on Image Processing
2022
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J. Wu, P. Lyu, G. Lu, C. Zhang, K. Yao, and W. Pei, “Decoupling recognition from detection: Single shot self-reliant scene text spotter,” in Proc. ACM Int. Conf. Multimedia
2022
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M. Ye, J. Zhang, S. Zhao, J. Liu, B. Du, and D. Tao, “DPText-DETR: Towards better scene text detection with dynamic points in transformer,” in Proc. AAAI Conf. Artificial Intell
2023
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