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Driven by deep neural networks and large scale datasets, scene text detection methods have progressed substantially over the past years, continuously refreshing the performance records on various standard benchmarks.
Object count/area graphs for the evaluation of object detection and segmentation algorithms
Wolf, C., Jolion, J.M.: · 2006
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Detecting text in natural scenes with stroke width transform
Epshtein, B., Ofek, E., Wexler, Y.: · 2010
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A method for text localization and recognition in real-world images
Neumann, L., Matas, J.: · 2010
Earlier work this paper cites.
Deconvolutional networks
Zeiler, M.D., Krishnan, D., Taylor, G.W., Fergus, R.: · 2010
Earlier work this paper cites.
Detecting texts of arbitrary orientations in natural images
Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z.: · 2012
Earlier work this paper cites.
Deep features for text spotting
Jaderberg, M., Vedaldi, A., Zisserman, A.: · 2014
Earlier work this paper cites.
Robust text detection in natural scene images
Yin, X.C., Yin, X., Huang, K., Hao, H.W.: · 2014
Earlier work this paper cites.
Robust scene text detection with convolution neural network induced mser trees
Huang, W., Qiao, Y., Tang, X.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
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A unified framework for multioriented text detection and recognition
Yao, C., Bai, X., Liu, W.: · 2014
Earlier work this paper cites.
Orientation robust text line detection in natural images
Kang, L., Li, Y., Doermann, D.: · 2014
Earlier work this paper cites.
Densebox: Unifying landmark localization with end to end object detection
Huang, L., Yang, Y., Deng, Y., Yu, Y.: · 2015
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Text detection and recognition in imagery: A survey
Ye, Q., Doermann, D.: · 2015
Earlier work this paper cites.
Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
Earlier work this paper cites.
Symmetry-based text line detection in natural scenes
Zhang, Z., Shen, W., Yao, C., Bai, X.: · 2015
Earlier work this paper cites.
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
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Fast r-cnn
Girshick, R.: · 2015
Cited alongside, same era.
Icdar 2015 competition on robust reading
Karatzas, D., Gomez-Bigorda, L., Nicolaou, A., Ghosh, S., Bagdanov, A., Iwamura, M., Matas, J., Neumann, L., Chandrasekhar, V.R., Lu, S., et al.: · 2015
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D., Ba, J.: · 2015
Cited alongside, same era.
Learning deconvolution network for semantic segmentation
Noh, H., Hong, S., Han, B.: · 2015
Cited alongside, same era.
The pascal visual object classes challenge: A retrospective
Everingham, M., Eslami, S.A., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: · 2015
Cited alongside, same era.
Scene text detection via holistic, multi-channel prediction
Yao, C., Bai, X., Sang, N., Zhou, X., Zhou, S., Cao, Z.: · 2016
EAST: An efficient and accurate scene text detector
Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., Liang, J.: · 2017
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Textboxes: A fast text detector with a single deep neural network
Liao, M., Shi, B., Bai, X., Wang, X., Liu, W.: · 2017
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Self-organized text detection with minimal post-processing via border learning
Wu, Y., Natarajan, P.: · 2017
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Multi-scale fcn with cascaded instance aware segmentation for arbitrary oriented word spotting in the wild
He, D., Yang, X., Liang, C., Zhou, Z., Ororbia, A.G., Kifer, D., Giles, C.L.: · 2017
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Wordsup: Exploiting word annotations for character based text detection
Hu, H., Zhang, C., Luo, Y., Wang, Y., Han, J., Ding, E.: · 2017
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Cited alongside, same era.
Reading text in the wild with convolutional neural networks
Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: · 2016
Cited alongside, same era.
Scene text detection and recognition: Recent advances and future trends
Zhu, Y., Yao, C., Bai, X.: · 2016
Cited alongside, same era.
SSD: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: · 2016
Cited alongside, same era.
Multi-oriented text detection with fully convolutional networks
Zhang, Z., Zhang, C., Shen, W., Yao, C., Liu, W., Bai, X.: · 2016
Cited alongside, same era.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
Cited alongside, same era.
Training region-based object detectors with online hard example mining
Shrivastava, A., Gupta, A., Girshick, R.: · 2016
Cited alongside, same era.
Tian, S., Lu, S., Li, C.: · 2017
Later among the works it cites.
Total-text: A comprehensive dataset for scene text detection and recognition
Kheng Chng, C., Chan, C.S.: · 2017
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Detecting curve text in the wild: New dataset and new solution
Yuliang, L., Lianwen, J., Shuaitao, Z., Sheng, Z.: · 2017
Later among the works it cites.
Arbitrary-oriented scene text detection via rotation proposals
Ma, J., Shao, W., Ye, H., Wang, L., Wang, H., Zheng, Y., Xue, X.: · 2017
Later among the works it cites.
Deep direct regression for multi-oriented scene text detection
He, W., Zhang, X.Y., Yin, F., Liu, C.L.: · 2017
Later among the works it cites.
Feature pyramid networks for object detection
Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: · 2017
Later among the works it cites.
Deep matching prior network: Toward tighter multi-oriented text detection
Liu, Y., Jin, L.: · 2017
Later among the works it cites.
Single shot text detector with regional attention
He, P., Huang, W., He, T., Zhu, Q., Qiao, Y., Li, X.: · 2017
Later among the works it cites.
Multi-oriented scene text detection via corner localization and region segmentation
Lyu, P., Yao, C., Wu, W., Yan, S., Bai, X.: · 2018
Closest in time.
Feature enhancement network: A refined scene text detector
Sheng, Z., Yuliang, L., Lianwen, J., Canjie, L.: · 2018
Closest in time.
Pixellink: Detecting scene text via instance segmentation
Deng, D., Liu, H., Li, X., Cai, D.: · 2018
Closest in time.