Abstract
Scene text detection has been significantly advanced over recent years, especially after the emergence of deep neural network. However, due to high diversity of scene texts in scale, orientation, shape and aspect ratio, as well as the inherent limitation of convolutional neural network for geometric transformations, to achieve accurate scene text detection is still an open problem. In this paper, we propose a novel sequential deformation method to effectively model the line-shape of scene text. An auxiliary character counting supervision is further introduced to guide the sequential offset prediction. The whole network can be easily optimized through an end-to-end multi-task manner. Extensive experiments are conducted on public scene text detection datasets including ICDAR 2017 MLT, ICDAR 2015, Total-text and SCUT-CTW1500. The experimental results demonstrate that the proposed method has outperformed previous state-of-the-art methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Baek, Y., Lee, B., Han, D., Yun, S., Lee, H.: Character region awareness for text detection. In: Proceedings CVPR, pp. 9365–9374 (2019)
Ch’ng, C.K., Chan, C.S.: Total-text: a comprehensive dataset for scene text detection and recognition. In: Proceedings ICDAR, pp. 935–942 (2017)
Dai, J., et al.: Deformable convolutional networks. In: Proceedings ICCV, pp. 764–773 (2017)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Li, F.F.: ImageNet: a large-scale hierarchical image database. In: Proceedings CVPR, pp. 248–255 (2009)
Feng, W., He, W., Yin, F., Zhang, X.Y., Liu, C.L.: TextDragon: an end-to-end framework for Arbitrary shaped text spotting. In: Proceedings ICCV, pp. 9076–9085 (2019)
Girshick, R.: FastR-CNN. In: Proceedings CVPR, pp. 1440–1448 (2015)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings ICCV, pp. 2961–2969 (2017)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings CVPR, pp. 770–778 (2016)
He, W., Zhang, X.Y., Yin, F., Liu, C.L.: Deep direct regression for multi-oriented scene text detection. In: Proceedings ICCV, pp. 745–753 (2017)
Huang, L., Yang, Y., Deng, Y., Yu, Y.: DenseBox: unifying landmark localization with end to end object detection. arXiv preprint arXiv:1509.04874 (2015)
Huang, Z., Zhong, Z., Sun, L., Huo, Q.: Mask R-CNN with pyramid attention network for scene text detection. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 764–772 (2019)
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Proceedings NIPS, pp. 2017–2025 (2015)
Karatzas, D., et al.: ICDAR 2015 competition on robust reading. In: Proceedings ICDAR, pp. 1156–1160 (2015)
Liao, M., Shi, B., Bai, X., Wang, X., Liu, W.: TextBoxes: a fast text detector with a single deep neural network. In: Proceedings AAAI, pp. 4161–4167 (2017)
Liao, M., Wan, Z., Yao, C., Chen, K., Bai, X.: Real-time Scene Text Detection with Differentiable Binarization. arXiv preprint arXiv:1911.08947 (2019)
Liao, M., Zhu, Z., Shi, B., Xia, G.S., Bai, X.: Rotation-sensitive regression for oriented scene text detection. In: Proceedings CVPR, pp. 5909–5918 (2018)
Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings CVPR, pp. 2117–2125 (2017)
Liu, J., Liu, X., Sheng, J., Liang, D., Li, X., Liu, Q.: Pyramid mask text detector. arXiv preprint arXiv:1903.11800 (2019)
Liu, W., et al.: SSD: single shot MultiBox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Liu, X., Liang, D., Yan, S., Chen, D., Qiao, Y., Yan, J.: FOTS: fast oriented text spotting with a unified network. In: Proceedings CVPR, pp. 5676–5685 (2018)
Liu, Y., Jin, L., Zhang, S., Zhang, S.: Detecting curve text in the wild: new dataset and new solution. arXiv preprint arXiv:1712.02170 (2017)
Liu, Y., Zhang, S., Jin, L., Xie, L., Wu, Y., Wang, Z.: Omnidirectional scene text detection with sequential-free box discretization. arXiv preprint arXiv:1906.02371 (2019)
Liu, Z., Lin, G., Yang, S., Liu, F., Lin, W., Goh, W.L.: Towards robust curve text detection with conditional spatial expansion. In: Proceedings CVPR, pp. 7269–7278 (2019)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings CVPR, pp. 3431–3440 (2015)
Long, S., He, X., Yao, C.: Scene text detection and recognition: the deep learning era. arXiv preprint arXiv:1811.04256 (2018)
Long, S., et al.: TextSnake: a flexible representation for detecting text of arbitrary shapes. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 19–35. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_2
Lyu, P., Liao, M., Yao, C., Wu, W., Bai, X.: Mask TextSpotter: an end-to-end trainable neural network for spotting text with arbitrary shapes. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 71–88. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_5
Lyu, P., Yao, C., Wu, W., Yan, S., Bai, X.: Multi-oriented scene text detection via corner localization and region segmentation. In: Proceedings CVPR, pp. 7553–7563 (2018)
Ma, J., et al.: Arbitrary-oriented scene text detection via rotation proposals. IEEE Trans. Multimedia, 3111–3122 (2018)
Nayef, N., et al.: ICDAR2017 robust reading challenge on multi-lingual scene text detection and script identification-RRC-MLT. In: Proceedings ICDAR, pp. 1454–1459 (2017)
Shi, B., Bai, X., Belongie, S.: Detecting oriented text in natural images by linking segments. In: Proceedings CVPR, pp. 2550–2558 (2017)
Shrivastava, A., Gupta, A., Girshick, R.: Training region-based object detectors with online hard example mining. In: Proceedings CVPR, pp. 761–769 (2016)
Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Proceedings NIPS, pp. 3104–3112 (2014)
Tian, Z., Huang, W., He, T., He, P., Qiao, Yu.: Detecting text in natural image with connectionist text proposal network. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 56–72. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_4
Tian, Z., et al.: Learning shape-aware embedding for scene text detection. In: Proceedings CVPR, pp. 4234–4243 (2019)
Vaswani, A., et al.: Attention is all you need. In: Proceedings NIPS, pp. 5998–6008 (2017)
Wang, F., Zhao, L., Li, X., Wang, X., Tao, D.: Geometry-aware scene text detection with instance transformation network. In: Proceedings CVPR, pp. 1381–1389 (2018)
Wang, W., et al.: Shape robust text detection with progressive scale expansion network. In: Proceedings CVPR, pp. 9336–9345 (2019)
Wang, W., et al.: Efficient and accurate arbitrary-shaped text detection with pixel aggregation network. In: Proceedings CVPR, pp. 8440–8449 (2019)
Wang, X., Jiang, Y., Luo, Z., Liu, C.L., Choi, H., Kim, S.: Arbitrary shape scene text detection with adaptive text region representation. In: Proceedings CVPR, pp. 6449–6458 (2019)
Wigington, C., Tensmeyer, C., Davis, B., Barrett, W., Price, B., Cohen, S.: Start, follow, read: end-to-end full-page handwriting recognition. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 372–388. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_23
Wikipedia: Eye movement in reading. https://en.wikipedia.org/wiki/Eye_movement_in_reading
Wu, W., Xing, J., Zhou, H.: TextCohesion: detecting text for arbitrary shapes. arXiv preprint arXiv:1904.12640 (2019)
Xie, E., Zang, Y., Shao, S., Yu, G., Yao, C., Li, G.: Scene text detection with supervised pyramid context network. In: Proceedings AAAI, pp. 9038–9045 (2019)
Xing, L., Tian, Z., Huang, W., Scott, M.R.: Convolutional character networks. In: Proceedings ICCV, pp. 9126–9136 (2019)
Xue, C., Lu, S., Zhan, F.: Accurate scene text detection through border semantics awareness and bootstrapping. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11220, pp. 370–387. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01270-0_22
Zhang, C., et al.: Look more than once: an accurate detector for text of arbitrary shapes. In: Proceedings CVPR, pp. 10552–10561 (2019)
Zhang, Z., He, T., Zhang, H., Zhang, Z., Xie, J., Li, M.: Bag of freebies for training object detection neural networks. arXiv preprint arXiv:1902.04103 (2019)
Zhou, X., et al.: EAST: an efficient and accurate scene text detector. In: Proceedings CVPR, pp. 5551–5560 (2017)
Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable convnets v2: more deformable, better results. In: Proceedings CVPR, pp. 9308–9316 (2019)
Acknowledgement
The authors would like to thank the reviewers for their valuable comments to improve the quality of the paper. This research is supported by a joint research project between Hyundai Motor Group AIRS Company and Tsinghua University. The second author is partially supported by National Key R&D Program of China and a grant from the Institute for Guo Qiang, Tsinghua University.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Xiao, S., Peng, L., Yan, R., An, K., Yao, G., Min, J. (2020). Sequential Deformation for Accurate Scene Text Detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12374. Springer, Cham. https://doi.org/10.1007/978-3-030-58526-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-58526-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-58525-9
Online ISBN: 978-3-030-58526-6
eBook Packages: Computer ScienceComputer Science (R0)