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PlugNet: Degradation Aware Scene Text Recognition Supervised by a Pluggable Super-Resolution Unit

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 12360)

Abstract

In this paper, we address the problem of recognizing degradation images that are suffering from high blur or low-resolution. We propose a novel degradation aware scene text recognizer with a pluggable super-resolution unit (PlugNet) to recognize low-quality scene text to solve this task from the feature-level. The whole networks can be trained end-to-end with a pluggable super-resolution unit (PSU) and the PSU will be removed after training so that it brings no extra computation. The PSU aims to obtain a more robust feature representation for recognizing low-quality text images. Moreover, to further improve the feature quality, we introduce two types of feature enhancement strategies: Feature Squeeze Module (FSM) which aims to reduce the loss of spatial acuity and Feature Enhance Module (FEM) which combines the feature maps from low to high to provide diversity semantics. As a consequence, the PlugNet achieves state-of-the-art performance on various widely used text recognition benchmarks like IIIT5K, SVT, SVTP, ICDAR15 and etc.

Keywords

Scene text recognition Neural network Feature learning 

Notes

Acknowledgement

This work was supported by the Project of the National Natural Science Foundation of China Grant No. 61977027 and No. 61702208, the Hubei Province Technological Innovation Major Project Grant No. 2019AAA044 and the Colleges Basic Research and Operation of MOE Grant No. CCNU19Z02002, CCNU18KFY02.

Supplementary material

504470_1_En_10_MOESM1_ESM.pdf (1.5 mb)
Supplementary material 1 (pdf 1581 KB)

References

  1. 1.
    Baek, J., et al.: What is wrong with scene text recognition model comparisons? dataset and model analysis. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4715–4723 (2019)Google Scholar
  2. 2.
    Bai, F., Cheng, Z., Niu, Y., Pu, S., Zhou, S.: Edit probability for scene text recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1508–1516 (2018)Google Scholar
  3. 3.
    Bai, Y., Zhang, Y., Ding, M., Ghanem, B.: Finding tiny faces in the wild with generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 21–30 (2018)Google Scholar
  4. 4.
    Bissacco, A., Cummins, M., Netzer, Y., Neven, H.: Photoocr: reading text in uncontrolled conditions. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 785–792 (2013)Google Scholar
  5. 5.
    Busta, M., Neumann, L., Matas, J.: Deep textspotter: an end-to-end trainable scene text localization and recognition framework. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2204–2212 (2017)Google Scholar
  6. 6.
    Cheng, Z., Bai, F., Xu, Y., Zheng, G., Pu, S., Zhou, S.: Focusing attention: towards accurate text recognition in natural images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5076–5084 (2017)Google Scholar
  7. 7.
    Cheng, Z., Xu, Y., Bai, F., Niu, Y., Pu, S., Zhou, S.: Aon: towards arbitrarily-oriented text recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5571–5579 (2018)Google Scholar
  8. 8.
    Cho, K., et al.: Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)
  9. 9.
    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
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  11. 11.
    Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Synthetic data and artificial neural networks for natural scene text recognition. arXiv preprint arXiv:1406.2227 (2014)
  12. 12.
    Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Deep structured output learning for unconstrained text recognition. arXiv preprint arXiv:1412.5903 (2015)
  13. 13.
    Jaderberg, M., Simonyan, K., Vedaldi, A., Zisserman, A.: Reading text in the wild with convolutional neural networks. Int. J. Comput. Vis. 116(1), 1–20 (2015).  https://doi.org/10.1007/s11263-015-0823-zMathSciNetCrossRefGoogle Scholar
  14. 14.
    Jaderberg, M., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017–2025 (2015)Google Scholar
  15. 15.
    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
  16. 16.
    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
  17. 17.
    Lee, C.Y., Osindero, S.: Recursive recurrent nets with attention modeling for ocr in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2231–2239 (2016)Google Scholar
  18. 18.
    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. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 67–83 (2019)Google Scholar
  19. 19.
    Liao, M., et al.: Scene text recognition from two-dimensional perspective. Proc. AAAI Conf. Artif. Intell. 33, 8714–8721 (2019)Google Scholar
  20. 20.
    Lim, B., Son, S., Kim, H., Nah, S., Mu Lee, K.: Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136–144 (2017)Google Scholar
  21. 21.
    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
  22. 22.
    Liu, W., Chen, C., Wong, K.Y.K.: Char-net: a character-aware neural network for distorted scene text recognition. Proc. AAAI 1(2), 4 (2018)Google Scholar
  23. 23.
    Liu, Y., Wang, Z., Jin, H., Wassell, I.: Synthetically supervised feature learning for scene text recognition. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 435–451 (2018)Google Scholar
  24. 24.
    Lucas, S.M., et al.: ICDAR 2003 robust reading competitions: entries, results, and future directions. Int. J. Doc. Anal. Recogn. 7(2–3), 105–122 (2005)CrossRefGoogle Scholar
  25. 25.
    Luo, C., Jin, L., Sun, Z.: Moran: a multi-object rectified attention network for scene text recognition. Pattern Recogn. 90, 109–118 (2019)CrossRefGoogle Scholar
  26. 26.
    Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
  27. 27.
    Mishra, A., Alahari, K., Jawahar, C.: Top-down and bottom-up cues for scene text recognition. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2687–2694. IEEE (2012)Google Scholar
  28. 28.
    Neumann, L., Matas, J.: Real-time scene text localization and recognition. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3538–3545. IEEE (2012)Google Scholar
  29. 29.
    Quy Phan, T., Shivakumara, P., Tian, S., Lim Tan, C.: Recognizing text with perspective distortion in natural scenes. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 569–576 (2013)Google Scholar
  30. 30.
    Risnumawan, A., Shivakumara, P., Chan, C.S., Tan, C.L.: A robust arbitrary text detection system for natural scene images. Expert Syst. Appl. 41(18), 8027–8048 (2014)CrossRefGoogle Scholar
  31. 31.
    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. IEEE Trans. Pattern Anal. Mach. Intell. 39(11), 2298–2304 (2016)CrossRefGoogle Scholar
  32. 32.
    Shi, B., Wang, X., Lyu, P., Yao, C., Bai, X.: Robust scene text recognition with automatic rectification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4168–4176 (2016)Google Scholar
  33. 33.
    Shi, B., Yang, M., Wang, X., Lyu, P., Yao, C., Bai, X.: Aster: an attentional scene text recognizer with flexible rectification. IEEE Trans. Pattern Anal. Mach. Intell. 41(9), 2035–2048 (2018)CrossRefGoogle Scholar
  34. 34.
    Su, B., Lu, S.: Accurate scene text recognition based on recurrent neural network. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9003, pp. 35–48. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-16865-4_3CrossRefGoogle Scholar
  35. 35.
    Wan, Z., He, M., Chen, H., Bai, X., Yao, C.: Textscanner: reading characters in order for robust scene text recognition. arXiv preprint arXiv:1912.12422 (2020)
  36. 36.
    Wang, J., Hu, X.: Gated recurrent convolution neural network for ocr. In: Advances in Neural Information Processing Systems, pp. 335–344 (2017)Google Scholar
  37. 37.
    Wang, K., Babenko, B., Belongie, S.: End-to-end scene text recognition. In: 2011 International Conference on Computer Vision, pp. 1457–1464. IEEE (2011)Google Scholar
  38. 38.
    Wang, K., Belongie, S.: Word spotting in the wild. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6311, pp. 591–604. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-15549-9_43CrossRefGoogle Scholar
  39. 39.
    Wang, W., et al.: Textsr: content-aware text super-resolution guided by recognition. arXiv:1909.07113 (2019)
  40. 40.
    Wang, X., et al.: Esrgan: enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV) (2018)Google Scholar
  41. 41.
    Wei, K., Yang, M., Wang, H., Deng, C., Liu, X.: Adversarial fine-grained composition learning for unseen attribute-object recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3741–3749 (2019)Google Scholar
  42. 42.
    Yang, M., et al.: Symmetry-constrained rectification network for scene text recognition. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 9147–9156 (2019)Google Scholar
  43. 43.
    Yao, C., Bai, X., Shi, B., Liu, W.: Strokelets: a learned multi-scale representation for scene text recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4042–4049 (2014)Google Scholar
  44. 44.
    Yin, X.C., Yin, X., Huang, K., Hao, H.W.: Robust text detection in natural scene images. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 970–983 (2013)Google Scholar
  45. 45.
    Zhan, F., Lu, S.: Esir: end-to-end scene text recognition via iterative image rectification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2059–2068 (2019)Google Scholar
  46. 46.
    Zhang, R.: Making convolutional networks shift-invariant again. arXiv preprint arXiv:1904.11486 (2019)
  47. 47.
    Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 286–301 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.AI -Labs, GuangZhou Image Data Technology Co., Ltd.GuangzhouChina
  2. 2.Nercel, Central China Normal UniversityWuhanChina

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