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End-to-end text-spotting, which aims to integrate detection and recognition in a unified framework, has attracted increasing attention due to its simplicity of the two complimentary tasks.
F. L. Bookstein, “Principal warps: Thin-plate splines and the decomposition of deformations,” IEEE Trans. Pattern Anal. Mach. Intell
1989
Earlier work this paper cites.
F. L. B. P. Warps, “Thin-plate splines and the decompositions of deformations,” IEEE Trans. Pattern Anal. Mach. Intell
1989
Earlier work this paper cites.
R. J. Williams and D. Zipser, “A learning algorithm for continually running fully recurrent neural networks,” Neural computation
1989
Earlier work this paper cites.
S. M. Lucas, A. Panaretos, L. Sosa, A. Tang, S. Wong, and R. Young, “ICDAR 2003 robust reading competitions,” in Proc. Int. Conf. Document Analysis and Recogn
2003
Earlier work this paper cites.
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks,” in Proc. Int. Conf. Mach. Learn
2006
Earlier work this paper cites.
B. Epshtein, E. Ofek, and Y. Wexler, “Detecting text in natural scenes with stroke width transform,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2010
Earlier work this paper cites.
P. Shivakumara, T. Q. Phan, and C. L. Tan, “A laplacian approach to multi-oriented text detection in video,” IEEE Trans. Pattern Anal. Mach. Intell
2010
Earlier work this paper cites.
A. Shahab, F. Shafait, and A. Dengel, “Icdar 2011 robust reading competition challenge 2: Reading text in scene images,” in 2011 international conference on document analysis and recognition
2011
Earlier work this paper cites.
R. Nagy, A. Dicker, and K. Meyer-Wegener, “NEOCR: A configurable dataset for natural image text recognition,” in Proc. Int. Workshop Camera-Based Document Analysis and Recognition
2011
Earlier work this paper cites.
C. Yao, X. Bai, W. Liu, Y. Ma, and Z. Tu, “Detecting texts of arbitrary orientations in natural images,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2012
Earlier work this paper cites.
L. Neumann and J. Matas, “Real-time scene text localization and recognition,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2012
Earlier work this paper cites.
D. Karatzas, F. Shafait, S. Uchida, et al
2013
Earlier work this paper cites.
W. Huang, Z. Lin, J. Yang, and J. Wang, “Text localization in natural images using stroke feature transform and text covariance descriptors,” in Proc. IEEE Int. Conf. Comp. Vis
2013
Earlier work this paper cites.
X.-C. Yin, X. Yin, K. Huang, and H.-W. Hao, “Robust text detection in natural scene images,” IEEE Trans. Pattern Anal. Mach. Intell
2013
Earlier work this paper cites.
W. Huang, Y. Qiao, and X. Tang, “Robust scene text detection with convolution neural network induced mser trees,” in Proc. Eur. Conf. Comp. Vis
2014
Earlier work this paper cites.
A. Risnumawan, P. Shivakumara, C.-S. Chan, and C. Tan, “A robust arbitrary text detection system for natural scene images,” in Expert Systems with Applications
2014
Earlier work this paper cites.
C. Yao, X. Bai, B. Shi, and W. Liu, “Strokelets: A learned multi-scale representation for scene text recognition,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2014
Earlier work this paper cites.
B. Su and S. Lu, “Accurate scene text recognition based on recurrent neural network,” in Asian Conference on Computer Vision
2014
Earlier work this paper cites.
M. Jaderberg, K. Simonyan, A. Zisserman, et al
2015
Earlier work this paper cites.
G. Liang, P. Shivakumara, T. Lu, and C. L. Tan, “Multi-spectral fusion based approach for arbitrarily oriented scene text detection in video images,” IEEE Trans. Image Process
2015
Earlier work this paper cites.
X.-C. Yin, W.-Y. Pei, J. Zhang, and H.-W. Hao, “Multi-orientation scene text detection with adaptive clustering,” IEEE Trans. Pattern Anal. Mach. Intell
2015
Earlier work this paper cites.
D. Karatzas, L. Gomez-Bigorda, et al
2015
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” in Proc. Int. Conf. Learn. Representations
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Proc. Advances in Neural Inf. Process. Syst
2015
Earlier work this paper cites.
J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell, “Long-term recurrent convolutional networks for visual recognition and description,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2015
Earlier work this paper cites.
Z. Tian, W. Huang, T. He, P. He, and Y. Qiao, “Detecting text in natural image with connectionist text proposal network,” in Proc. Eur. Conf. Comp. Vis
2016
Earlier work this paper cites.
Z. Zhang, C. Zhang, W. Shen, C. Yao, W. Liu, and X. Bai, “Multi-oriented text detection with fully convolutional networks,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2016
Earlier work this paper cites.
B. Shi, X. Wang, P. Lyu, C. Yao, and X. Bai, “Robust scene text recognition with automatic rectification,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in Proc. Eur. Conf. Comp. Vis
2016
Earlier work this paper cites.
A. Veit, T. Matera, L. Neumann, J. Matas, and S. Belongie, “Coco-text: Dataset and benchmark for text detection and recognition in natural images,” arXiv: Comp. Res. Repository
2016
Earlier work this paper cites.
A. Gupta, A. Vedaldi, and A. Zisserman, “Synthetic data for text localisation in natural images,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2016
Earlier work this paper cites.
H. Li, P. Wang, and C. Shen, “Towards end-to-end text spotting with convolutional recurrent neural networks,” in Proc. IEEE Int. Conf. Comp. Vis
2017
Earlier work this paper cites.
M. Liao, B. Shi, X. Bai, X. Wang, and W. Liu, “Textboxes: A fast text detector with a single deep neural network,” in Proc. AAAI Conf. Artificial Intell
2017
Earlier work this paper cites.
B. Shi, X. Bai, and S. Belongie, “Detecting oriented text in natural images by linking segments,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2017
Cited alongside, same era.
Y. Liu and L. Jin, “Deep matching prior network: Toward tighter multi-oriented text detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2017
Cited alongside, same era.
X. Zhou, C. Yao, H. Wen, Y. Wang, S. Zhou, W. He, and J. Liang, “EAST: An efficient and accurate scene text detector,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2017
Cited alongside, same era.
H. Hu, C. Zhang, Y. Luo, Y. Wang, J. Han, and E. Ding, “Wordsup: Exploiting word annotations for character based text detection,” in Proc. IEEE Int. Conf. Comp. Vis
2017
Cited alongside, same era.
B. Shi, C. Yao, M. Liao, M. Yang, P. Xu, L. Cui, S. Belongie, S. Lu, and X. Bai, “Icdar2017 competition on reading chinese text in the wild (rctw-17),” in Proc. IAPR Int. Conf. Document Analysis and Recognition
W. Wang, E. Xie, X. Li, W. Hou, T. Lu, G. Yu, and S. Shao, “Shape Robust Text Detection with Progressive Scale Expansion Network,” Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2019
Later among the works it cites.
J. Tang, Z. Yang, Y. Wang, Q. Zheng, Y. Xu, and X. Bai, “Seglink++: Detecting dense and arbitrary-shaped scene text by instance-aware component grouping,” Pattern Recognition
2019
Later among the works it cites.
C. Zhang, B. Liang, Z. Huang, M. En, J. Han, E. Ding, and X. Ding, “Look More Than Once: An Accurate Detector for Text of Arbitrary Shapes,” Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2019
Later among the works it cites.
C. Xue, S. Lu, and W. Zhang, “MSR: Multi-Scale Shape Regression for Scene Text Detection,” Proc. Int. Joint Conf. Artificial Intell
2019
Later among the works it cites.
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2017
Cited alongside, same era.
B. Su and S. Lu, “Accurate recognition of words in scenes without character segmentation using recurrent neural network,” Pattern Recognition
2017
Cited alongside, same era.
B. Shi, X. Bai, and C. Yao, “An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition,” IEEE Trans. Pattern Anal. Mach. Intell
2017
Cited alongside, same era.
M. Busta, L. Neumann, and J. Matas, “Deep textspotter: An end-to-end trainable scene text localization and recognition framework,” in Proc. IEEE Int. Conf. Comp. Vis
2017
Cited alongside, same era.
W. He, X.-Y. Zhang, F. Yin, and C.-L. Liu, “Deep direct regression for multi-oriented scene text detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2017
Cited alongside, same era.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” in Proc. IEEE Int. Conf. Comp. Vis
2017
Cited alongside, same era.
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie, “Feature pyramid networks for object detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2017
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.
2019
Later among the works it cites.
C. Luo, L. Jin, and Z. Sun, “Moran: A multi-object rectified attention network for scene text recognition,” Pattern Recognition
2019
Later among the works it cites.
F. Zhan and S. Lu, “Esir: End-to-end scene text recognition via iterative image rectification,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
2019
Later among the works it cites.
H. Li, P. Wang, C. Shen, and G. Zhang, “Show, attend and read: A simple and strong baseline for irregular text recognition,” in Proc. AAAI Conf. Artificial Intell
2019
Later among the works it cites.
M. Liao, J. Zhang, Z. Wan, F. Xie, J. Liang, P. Lyu, C. Yao, and X. Bai, “Scene text recognition from two-dimensional perspective,” in Proc. AAAI Conf. Artificial Intell
2019
Later among the works it cites.
Y. Xu, Y. Wang, W. Zhou, Y. Wang, Z. Yang, and X. Bai, “Textfield: Learning a deep direction field for irregular scene text detection,” IEEE Trans. Image Process
2019
Later among the works it cites.
Z. Zhong, L. Sun, and Q. Huo, “An anchor-free region proposal network for faster r-cnn-based text detection approaches,” Int. J. Document Analysis Recogn
2019
Later among the works it cites.
Z. Tian, C. Shen, H. Chen, and T. He, “Fcos: Fully convolutional one-stage object detection,” in Proc. IEEE Int. Conf. Comp. Vis
2019
Later among the works it cites.
Z. Tian, M. Shu, P. Lyu, R. Li, C. Zhou, X. Shen, and J. Jia, “Learning Shape-Aware Embedding for Scene Text Detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2019
Later among the works it cites.
Y. Sun, Z. Ni, C.-K. Chng, Y. Liu, C. Luo, C. C. Ng, J. Han, E. Ding, J. Liu, D. Karatzas, et al
2019
Later among the works it cites.
C.-K. Chng, Y. Liu, Y. Sun, C. C. Ng, C. Luo, Z. Ni, C. Fang, S. Zhang, J. Han, E. Ding, et al
2019
Later among the works it cites.
Y. Liu, L. Jin, and C. Fang, “Arbitrarily shaped scene text detection with a mask tightness text detector,” IEEE Trans. Image Process
2019
Later among the works it cites.
2019
Later among the works it cites.
H. Wang, P. Lu, H. Zhang, M. Yang, X. Bai, Y. Xu, M. He, Y. Wang, and W. Liu, “All you need is boundary: Toward arbitrary-shaped text spotting,” in Proc. AAAI Conf. Artificial Intell
2020
Later among the works it cites.
M. Tan, R. Pang, and Q. V. Le, “Efficientdet: Scalable and efficient object detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Later among the works it cites.
X. Wang, R. Zhang, T. Kong, L. Li, and C. Shen, “Solov2: Dynamic and fast instance segmentation,” in Proc. Advances in Neural Inf. Process. Syst
2020
Later among the works it cites.
Y. Liu, H. Chen, C. Shen, T. He, L. Jin, and L. Wang, “Abcnet: Real-time scene text spotting with adaptive bezier-curve network,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Later among the works it cites.
M. Liao, Z. Wan, C. Yao, K. Chen, and X. Bai, “Real-time scene text detection with differentiable binarization.,” in Proc. AAAI Conf. Artificial Intell
2020
Later among the works it cites.
Y. Wang, H. Xie, Z.-J. Zha, M. Xing, Z. Fu, and Y. Zhang, “Contournet: Taking a further step toward accurate arbitrary-shaped scene text detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Later among the works it cites.
S.-X. Zhang, X. Zhu, J.-B. Hou, C. Liu, C. Yang, H. Wang, and X.-C. Yin, “Deep relational reasoning graph network for arbitrary shape text detection,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Later among the works it cites.
R. Litman, O. Anschel, S. Tsiper, R. Litman, S. Mazor, and R. Manmatha, “SCATTER: selective context attentional scene text recognizer,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn
2020
Later among the works it cites.
X. Yue, Z. Kuang, C. Lin, H. Sun, and W. Zhang, “RobustScanner: Dynamically enhancing positional clues for robust text recognition,” in Proc. Eur. Conf. Comp. Vis
2020
Later among the works it cites.
Z. Wan, M. He, H. Chen, X. Bai, and C. Yao, “Textscanner: Reading characters in order for robust scene text recognition,” in Proc. AAAI Conf. Artificial Intell
2020
Later among the works it cites.
M. Liao, G. Pang, J. Huang, T. Hassner, and X. Bai, “Mask textspotter v3: Segmentation proposal network for robust scene text spotting,” in Proc. Eur. Conf. Comp. Vis
2020
Later among the works it cites.
T. Wang, Y. Zhu, L. Jin, C. Luo, X. Chen, Y. Wu, Q. Wang, and M. Cai, “Decoupled attention network for text recognition.,” in Proc. AAAI Conf. Artificial Intell
2020
Later among the works it cites.
S. K. Esser, J. L. McKinstry, D. Bablani, R. Appuswamy, and D. S. Modha, “Learned step size quantization,” in Proc. Int. Conf. Learn. Representations
2020
Later among the works it cites.
Y. Baek, S. Shin, J. Baek, S. Park, J. Lee, D. Nam, and H. Lee, “Character region attention for text spotting,” in Proc. Eur. Conf. Comp. Vis
2020
Later among the works it cites.
W. Feng, F. Yin, X.-Y. Zhang, W. He, and C.-L. Liu, “Residual dual scale scene text spotting by fusing bottom-up and top-down processing,” Int. J. Comput. Vision
2020
Later among the works it cites.