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Rotated Box Is Back: An Accurate Box Proposal Network for Scene Text Detection

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Document Analysis and Recognition – ICDAR 2021 (ICDAR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12824))

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Abstract

Scene text detection is a challenging task because it must be able to handle text in various fonts and from various perspective views. This makes it difficult to use rectangular bounding boxes to detect text locations accurately. To detect multi-oriented text, rotated bounding box-based methods have been explored as an alternative. However, they are not as accurate for scene text detection as rectangular bounding box-based methods. In this paper, we propose a novel region-proposal network to suggest rotated bounding boxes and an iterative region refinement network for final scene text detection. The proposed region-proposal network predicts rotated box candidates from pixels and anchors, which increases recall by creating more candidates around the text. The proposed refinement network improves the accuracy of scene text detection by correcting the differences in the locations between the ground truth and the prediction. In addition, we reduce the backpropagation time by using a new pooling method called rotated box crop and resize pooling. The proposed method achieved state-of-the-art performance on ICDAR 2017, that is, an f-score of 75.0% and competitive results with f-scores of 86.9% and 92.4% on ICDAR 2015 and ICDAR 2013, respectively. Furthermore, our approach achieves a significant increase in performance over previous methods based on rotated bounding boxes.

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References

  1. Xie, E., Zang, Y., Shao, S., Yu, G., Yao, C., Li, G.: Scene text detection with supervised pyramid context network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9038–9045 (2019)

    Google Scholar 

  2. Huang, Z., Zhong, Z., Sun, L., Huo, Q.: Mask R-CNN with pyramid attention network for scene text detection. In: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 764–772. IEEE (2019)

    Google Scholar 

  3. Wang, W., et al.: Shape robust text detection with progressive scale expansion network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9336–9345 (2019)

    Google Scholar 

  4. Ma, J., et al.: Arbitrary-oriented scene text detection via rotation proposals. IEEE Trans. Multimed. 20(11), 3111–3122 (2018)

    Article  Google Scholar 

  5. Zhou, X., et al.: East: an efficient and accurate scene text detector. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5551–5560 (2017)

    Google Scholar 

  6. Liao, M., Shi, B., Bai, X.: Textboxes++: a single-shot oriented scene text detector. IEEE Trans. Image Process. 27(8), 3676–3690 (2018)

    Article  MathSciNet  Google Scholar 

  7. Nayef, N., et al.: ICDAR 2017 robust reading challenge on multi-lingual scene text detection and script identification-RRC-MLT. In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1454–1459. IEEE (2017)

    Google Scholar 

  8. Karatzas, D., et al.: ICDAR 2013 robust reading competition. In: 2013 12th International Conference on Document Analysis and Recognition, pp. 1484–1493. IEEE (2013)

    Google Scholar 

  9. Karatzas, D., et al.: ICDAR 2015 competition on robust reading. In: 2015 13th International Conference on Document Analysis and Recognition (ICDAR), pp. 1156–1160. IEEE (2015)

    Google Scholar 

  10. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)

    Google Scholar 

  11. Li, Y., et al.: Pixel-anchor: a fast oriented scene text detector with combined networks. arXiv preprint arXiv:1811.07432 (2018)

  12. Zhang, C., et al.: Look more than once: an accurate detector for text of arbitrary shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10552–10561 (2019)

    Google Scholar 

  13. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

  14. Gupta, A., Vedaldi, A., Zisserman, A.: Synthetic data for text localisation in natural images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2315–2324 (2016)

    Google Scholar 

  15. ICDAR 2017 competition on multi-lingual scene text detection and script identification. https://rrc.cvc.uab.es/?ch=8&com=evaluation&task=1

  16. Zhong, Z., Sun, L., Huo, Q.: An anchor-free region proposal network for faster R-CNN-based text detection approaches. Int. J. Document Anal. Recogn. (IJDAR) 22(3), 315–327 (2019)

    Article  Google Scholar 

  17. Liu, X., Liang, D., Yan, S., Chen, D., Qiao, Y., Yan, J.: FOTS: fast oriented text spotting with a unified network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5676–5685 (2018)

    Google Scholar 

  18. Liao, M., Zhu, Z., Shi, B., Xia, G.S., Bai, X.: Rotation-sensitive regression for oriented scene text detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5909–5918 (2018)

    Google Scholar 

  19. Wang, Y., Xie, H., Zha, Z.J., Xing, M., Fu, Z., Zhang, Y.: ContourNet: taking a further step toward accurate arbitrary-shaped scene text detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11753–11762 (2020)

    Google Scholar 

  20. Zhang, S., Liu, Y., Jin, L., Luo, C.: Feature enhancement network: a refined scene text detector. arXiv preprint arXiv:1711.04249 (2017)

  21. Baek, Y., Lee, B., Han, D., Yun, S., Lee, H.: Character region awareness for text detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9365–9374 (2019)

    Google Scholar 

  22. Xu, Y., et al.: Geometry normalization networks for accurate scene text detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9137–9146 (2019)

    Google Scholar 

  23. Liao, M., Wan, Z., Yao, C., Chen, K., Bai, X.: Real-time scene text detection with differentiable binarization. In: AAAI, pp. 11474–11481 (2020)

    Google Scholar 

  24. Xu, Y., Wang, Y., Zhou, W., Wang, Y., Yang, Z., Bai, X.: TextField: learning a deep direction field for irregular scene text detection. IEEE Trans. Image Process. 28(11), 5566–5579 (2019)

    Article  MathSciNet  Google Scholar 

  25. Deng, D., Liu, H., Li, X., Cai, D.: PixelLink: detecting scene text via instance segmentation. arXiv preprint arXiv:1801.01315 (2018)

  26. Tian, Z., et al.: Learning shape-aware embedding for scene text detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4234–4243 (2019)

    Google Scholar 

  27. Xue, C., Lu, S., Zhan, F.: Accurate scene text detection through border semantics awareness and bootstrapping. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 355–372 (2018)

    Google Scholar 

  28. Xing, L., Tian, Z., Huang, W., Scott, M.R.: Convolutional character networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9126–9136 (2019)

    Google Scholar 

  29. Wang, P., et al.: A single-shot arbitrarily-shaped text detector based on context attended multi-task learning. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1277–1285 (2019)

    Google Scholar 

  30. 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: Proceedings of the European Conference on Computer Vision (ECCV), pp. 67–83 (2018)

    Google Scholar 

  31. Yang, Q., et al.: IncepText: a new inception-text module with deformable PSROI pooling for multi-oriented scene text detection. arXiv preprint arXiv:1805.01167 (2018)

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Acknowledgements

This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 1711125972, Audio-Visual Perception for Autonomous Rescue Drones).

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Authors and Affiliations

  1. Vision AI Lab, AI Center, NCSOFT, Gyeonggi-do, Republic of Korea

    Jusung Lee, Jaemyung Lee, Cheoljong Yang, Younghyun Lee & Joonsoo Lee

Authors
  1. Jusung Lee
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  2. Jaemyung Lee
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  3. Cheoljong Yang
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  4. Younghyun Lee
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  5. Joonsoo Lee
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Correspondence to Joonsoo Lee .

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Editors and Affiliations

  1. Universitat Autònoma de Barcelona, Barcelona, Spain

    Josep Lladós

  2. Lehigh University, Bethlehem, PA, USA

    Daniel Lopresti

  3. Kyushu University, Fukuoka-shi, Japan

    Seiichi Uchida

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Lee, J., Lee, J., Yang, C., Lee, Y., Lee, J. (2021). Rotated Box Is Back: An Accurate Box Proposal Network for Scene Text Detection. In: Lladós, J., Lopresti, D., Uchida, S. (eds) Document Analysis and Recognition – ICDAR 2021. ICDAR 2021. Lecture Notes in Computer Science(), vol 12824. Springer, Cham. https://doi.org/10.1007/978-3-030-86337-1_4

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  • DOI: https://doi.org/10.1007/978-3-030-86337-1_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86336-4

  • Online ISBN: 978-3-030-86337-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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