Skip to main content

Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting

  • Conference paper
  • First Online:
Computer Vision – ECCV 2020 (ECCV 2020)

Abstract

Recent end-to-end trainable methods for scene text spotting, integrating detection and recognition, showed much progress. However, most of the current arbitrary-shape scene text spotters use region proposal networks (RPN) to produce proposals. RPN relies heavily on manually designed anchors and its proposals are represented with axis-aligned rectangles. The former presents difficulties in handling text instances of extreme aspect ratios or irregular shapes, and the latter often includes multiple neighboring instances into a single proposal, in cases of densely oriented text. To tackle these problems, we propose Mask TextSpotter v3, an end-to-end trainable scene text spotter that adopts a Segmentation Proposal Network (SPN) instead of an RPN. Our SPN is anchor-free and gives accurate representations of arbitrary-shape proposals. It is therefore superior to RPN in detecting text instances of extreme aspect ratios or irregular shapes. Furthermore, the accurate proposals produced by SPN allow masked RoI features to be used for decoupling neighboring text instances. As a result, our Mask TextSpotter v3 can handle text instances of extreme aspect ratios or irregular shapes, and its recognition accuracy won’t be affected by nearby text or background noise. Specifically, we outperform state-of-the-art methods by 21.9% on the Rotated ICDAR 2013 dataset (rotation robustness), 5.9% on the Total-Text dataset (shape robustness), and achieve state-of-the-art performance on the MSRA-TD500 dataset (aspect ratio robustness). Code is available at: https://github.com/MhLiao/MaskTextSpotterV3

M. Liao—Work done while an intern at Facebook.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://github.com/MhLiao/MaskTextSpotter.

  2. 2.

    https://github.com/MalongTech/research-charnet.

References

  1. Baek, Y., Lee, B., Han, D., Yun, S., Lee, H.: Character region awareness for text detection. In: Proceeding Conference Computer Vision Pattern Recognition, pp. 9365–9374 (2019)

    Google Scholar 

  2. Bissacco, A., Cummins, M., Netzer, Y., Neven, H.: PhotoOCR: reading text in uncontrolled conditions. In: Proceeding International Conference Computer Vision, pp. 785–792 (2013)

    Google Scholar 

  3. Busta, M., Neumann, L., Matas, J.: Deep textspotter: an end-to-end trainable scene text localization and recognition framework. In: Proceeding International Conference Computer Vision, pp. 2223–2231 (2017)

    Google Scholar 

  4. Ch’ng, C.K., Chan, C.S.: Total-text: a comprehensive dataset for scene text detection and recognition. In: Proceeding International Conference on Document Analysis and Recognition, pp. 935–942 (2017)

    Google Scholar 

  5. Ch’ng, C.-K., Chan, C.S., Liu, C.-L.: Total-Text: toward orientation robustness in scene text detection. Int. J. Doc. Anal. Recogn. (IJDAR) 23(1), 31–52 (2019). https://doi.org/10.1007/s10032-019-00334-z

    Article  Google Scholar 

  6. Deng, D., Liu, H., Li, X., Cai, D.: Pixellink: detecting scene text via instance segmentation. In: AAAI Conference on Artificial Intelligence (2018)

    Google Scholar 

  7. Feng, W., He, W., Yin, F., Zhang, X.Y., Liu, C.L.: TextDragon: an end-to-end framework for arbitrary shaped text spotting. In: Proceeding International Conference Computer Vision (2019)

    Google Scholar 

  8. Girshick, R.B.: Fast R-CNN. In: Proceeding International Conference Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  9. Gupta, A., Vedaldi, A., Zisserman, A.: Synthetic data for text localisation in natural images. In: Proceeding Conference on Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  10. Hassner, T., Rehbein, M., Stokes, P.A., Wolf, L.: Computation and palaeography: potentials and limits. Dagstuhl Rep. 2(9), 184–199 (2012)

    Google Scholar 

  11. He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceeding Conference Computer Vision Pattern Recognition, pp. 2961–2969 (2017)

    Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceeding Conference Computer Vision Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  13. He, T., Huang, W., Qiao, Y., Yao, J.: Accurate text localization in natural image with cascaded convolutional text network. CoRR abs/1603.09423 (2016)

    Google Scholar 

  14. He, T., Huang, W., Qiao, Y., Yao, J.: Text-attentional convolutional neural network for scene text detection. Trans. Image Process. 25(6), 2529–2541 (2016)

    Article  MathSciNet  Google Scholar 

  15. He, T., Tian, Z., Huang, W., Shen, C., Qiao, Y., Sun, C.: An end-to-end textspotter with explicit alignment and attention. In: Proceeding Conference Computer Vision Pattern Recognition, pp. 5020–5029 (2018)

    Google Scholar 

  16. He, W., Zhang, X., Yin, F., Liu, C.: Deep direct regression for multi-oriented scene text detection. In: Proceeding Conference Computer Vision Pattern Recognition (2017)

    Google Scholar 

  17. Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Reading text in the wild with convolutional neural networks. Int. J. Comput. Vision 116(1), 1–20 (2016). https://doi.org/10.1007/s11263-015-0823-z

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

    Google Scholar 

  19. Karatzas, D., et al.: ICDAR 2013 robust reading competition. In: Proceedings of the International Conference on Document Analysis and Recognition, pp. 1484–1493 (2013)

    Google Scholar 

  20. Li, H., Wang, P., Shen, C.: Towards end-to-end text spotting with convolutional recurrent neural networks. In: Proceedings International Conference Computer Vision, pp. 5248–5256 (2017)

    Google Scholar 

  21. Liao, M., Lyu, P., He, M., Yao, C., Wu, W., Bai, X.: Mask TextSpotter: An end-to-end trainable neural network for spotting text with arbitrary shapes. Trans. Pattern Anal. Mach. Intell., 1–1 (2019)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  23. Liao, M., Shi, B., Bai, X., Wang, X., Liu, W.: TextBoxes: A fast text detector with a single deep neural network. In: AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  26. Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceeding Conference Computer Vision Pattern Recognition, pp. 2117–2125 (2017)

    Google Scholar 

  27. Liu, X., Liang, D., Yan, S., Chen, D., Qiao, Y., Yan, J.: FOTS: Fast oriented text spotting with a unified network. In: Proceeding Conference Computer Vision Pattern Recognition, pp. 5676–5685 (2018)

    Google Scholar 

  28. Liu, Y., Chen, H., Shen, C., He, T., Jin, L., Wang, L.: Abcnet: Real-time scene text spotting with adaptive bezier-curve network. In: Proceeding Conference Computer Vision Pattern Recognition, pp. 9809–9818 (2020)

    Google Scholar 

  29. Liu, Z., Lin, G., Yang, S., Feng, J., Lin, W., Goh, W.L.: Learning markov clustering networks for scene text detection. In: Proceeding Conference Computer Vision Pattern Recognition, pp. 6936–6944 (2018)

    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: European Conference Computer Vision, pp. 71–88 (2018)

    Google Scholar 

  31. Lyu, P., Yao, C., Wu, W., Yan, S., Bai, X.: Multi-oriented scene text detection via corner localization and region segmentation. In: Proceedings Conference Computer Vision Pattern Recognition, pp. 7553–7563 (2018)

    Google Scholar 

  32. Milletari, F., Navab, N., Ahmadi, S.A.: V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: International Conference on 3D Vision, pp. 565–571 (2016)

    Google Scholar 

  33. Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: European Conference Computer Vision, pp. 483–499 (2016)

    Google Scholar 

  34. Qin, S., Bissacco, A., Raptis, M., Fujii, Y., Xiao, Y.: Towards unconstrained end-to-end text spotting. In: Proceedings of the International Conference Computer Vision (2019)

    Google Scholar 

  35. Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Neural Information Processing System, 91–99 (2015)

    Google Scholar 

  36. Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: Int. Conf. on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)

    Google Scholar 

  37. Shi, B., Bai, X., Yao, C.: An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition. Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2017)

    Article  Google Scholar 

  38. Tian, Z., Shu, M., Lyu, P., Li, R., Zhou, C., Shen, X., Jia, J.: Learning shape-aware embedding for scene text detection. In: Proceeding Conference Computer Vision Pattern Recognition, pp. 4234–4243 (2019)

    Google Scholar 

  39. Vatti, B.R.: A generic solution to polygon clipping. Commun. ACM 35(7), 56–64 (1992)

    Article  Google Scholar 

  40. Wang, H., et al.: All you need is boundary: toward arbitrary-shaped text spotting. In: AAAI Conference on Artificial Intelligence, pp. 12160–12167 (2020)

    Google Scholar 

  41. Wang, T., Wu, D.J., Coates, A., Ng, A.Y.: End-to-end text recognition with convolutional neural networks. In: International Conference Pattern Recognition (2012)

    Google Scholar 

  42. Wang, W., Xie, E., Li, X., Hou, W., Lu, T., Yu, G., Shao, S.: Shape robust text detection with progressive scale expansion network. In: Proceeding Conference Computer Vision Pattern Recognition, pp. 9336–9345 (2019)

    Google Scholar 

  43. Xing, L., Tian, Z., Huang, W., Scott, M.R.: Convolutional character networks. In: Proceeding Conference Computer Vision Pattern Recognition (2019)

    Google Scholar 

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

    Google Scholar 

  45. Yao, C., Bai, X., Liu, W., Ma, Y., Tu, Z.: Detecting texts of arbitrary orientations in natural images. In: Proceeding Conference Computer Vision Pattern Recognition (2012)

    Google Scholar 

  46. Zhan, F., Xue, C., Lu, S.: GA-DAN: geometry-aware domain adaptation network for scene text detection and recognition. In: Proceeding Conference Computer Vision Pattern Recognition (2019)

    Google Scholar 

  47. Zhang, Z., Zhang, C., Shen, W., Yao, C., Liu, W., Bai, X.: Multi-oriented text detection with fully convolutional networks. In: Proceeding Conference Computer Vision Pattern Recognition, pp. 4159–4167 (2016)

    Google Scholar 

  48. Zhong, Z., Jin, L., Zhang, S., Feng, Z.: DeepText: A unified framework for text proposal generation and text detection in natural images. CoRR abs/1605.07314 (2016)

    Google Scholar 

  49. Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., Liang, J.: EAST: an efficient and accurate scene text detector. In: Proceeding Conference Computer Vision Pattern Recognition, pp. 2642–2651 (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiang Bai .

Editor information

Editors and Affiliations

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 1438 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Liao, M., Pang, G., Huang, J., Hassner, T., Bai, X. (2020). Mask TextSpotter v3: Segmentation Proposal Network for Robust Scene Text Spotting. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12356. Springer, Cham. https://doi.org/10.1007/978-3-030-58621-8_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58621-8_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58620-1

  • Online ISBN: 978-3-030-58621-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics