Skip to main content
SpringerLink
Log in

OSTER: An Orientation Sensitive Scene Text Recognizer with CenterLine Rectification

  • Conference paper
  • First Online:
Pattern Recognition (ACPR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12046))

Included in the following conference series:

Abstract

Scene texts in China are always arbitrarily arranged in two forms: horizontally and vertically. These two forms of texts exhibit distinctive features, making it difficult to recognize them simultaneously. Besides, recognizing irregular scene texts is still a challenging task due to their various shapes and distorted patterns. In this paper, we propose an orientation sensitive network aiming at distinguishing between Chinese horizontal and vertical texts. The learned orientation is then passed into an attention selective network to adjust the attention maps of the sequence recognition model, leading it working for each type of texts respectively. In addition, a lightweight centerline rectification network is adopted, which enables the irregular texts more readable while no redundant labels are needed. A synthetic dataset named SCTD is released to support our training and evaluate the proposed model. Extensive experiments show that the proposed method is capable of recognizing arbitrarily-aligned scene texts accurately and efficiently, achieving state-of-the-art performance over a number of public datasets.

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

Access this chapter

Log in via an institution
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

References

  1. 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 

  2. 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 

  3. 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 

  4. 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 

  5. Graves, A., Schmidhuber, J.: Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw. 18(5–6), 602–610 (2005)

    Article  Google Scholar 

  6. 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 

  7. He, M., et al.: ICPR 2018 contest on robust reading for multi-type web images. In: 2018 24th International Conference on Pattern Recognition (ICPR), pp. 7–12. IEEE (2018)

    Google Scholar 

  8. ICDAR2019: ICDAR 2019 robust reading challenge on large-scale street view text with partial labeling. https://rrc.cvc.uab.es/?ch=16. Accessed 20 Apr 2019

  9. 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)

  10. Jaderberg, M., Simonyan, K., Zisserman, A., et al.: Spatial transformer networks. In: Advances in Neural Information Processing Systems, pp. 2017–2025 (2015)

    Google Scholar 

  11. Jaderberg, M., Vedaldi, A., Zisserman, A.: Deep features for text spotting. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8692, pp. 512–528. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10593-2_34

    Chapter  Google Scholar 

  12. 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 

  13. 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 

  14. Liao, M., et al.: Scene text recognition from two-dimensional perspective. arXiv preprint arXiv:1809.06508 (2018)

  15. Liu, Z., Li, Y., Ren, F., Yu, H.: A binary convolutional encoder-decoder network for real-time natural scene text processing. arXiv preprint arXiv:1612.03630 (2016)

  16. Luo, C., Jin, L., Sun, Z.: MORAN: a multi-object rectified attention network for scene text recognition. Pattern Recogn. 90, 109–118 (2019)

    Article  Google Scholar 

  17. Mishra, A., Alahari, K., Jawahar, C.: Scene text recognition using higher order language priors. In: BMVC-British Machine Vision Conference. BMVA (2012)

    Google Scholar 

  18. 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)

    Article  Google Scholar 

  19. Rodriguez-Serrano, J.A., Gordo, A., Perronnin, F.: Label embedding: a frugal baseline for text recognition. Int. J. Comput. Vis. 113(3), 193–207 (2015)

    Article  Google Scholar 

  20. 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 (2017)

    Article  Google Scholar 

  21. 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 

  22. 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, 2035–2048 (2018)

    Article  Google Scholar 

  23. Shi, B., et al.: ICDAR 2017 competition on reading Chinese text in the wild (RCTW-17). In: 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), vol. 1, pp. 1429–1434. IEEE (2017)

    Google Scholar 

  24. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems, pp. 3104–3112 (2014)

    Google Scholar 

  25. Wang, J., Hu, X.: Gated recurrent convolution neural network for OCR. In: Advances in Neural Information Processing Systems, pp. 335–344 (2017)

    Google Scholar 

  26. 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 

  27. Wang, P., Yang, L., Li, H., Deng, Y., Shen, C., Zhang, Y.: A simple and robust convolutional-attention network for irregular text recognition. arXiv preprint arXiv:1904.01375 (2019)

  28. Yang, X., He, D., Zhou, Z., Kifer, D., Giles, C.L.: Learning to read irregular text with attention mechanisms. In: IJCAI, pp. 3280–3286 (2017)

    Google Scholar 

  29. 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 

Download references

Acknowledgment

This work is supported by the Key Programs of the Chinese Academy of Sciences under Grant No. ZDBS-SSWJSC003, No. ZDBS-SSW-JSC004, and No. ZDBS-SSWJSC005, and the National Natural Science Foundation of China (NSFC) under Grant No. 61601462, No. 61531019, and No. 71621002.

Author information

Authors and Affiliations

  1. The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation Chinese Academy of Sciences, Beijing, China

    Zipeng Feng, Chen Du, Yanna Wang & Baihua Xiao

  2. University of Chinese Academy of Sciences, Beijing, China

    Zipeng Feng & Chen Du

Authors
  1. Zipeng Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yanna Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Baihua Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Baihua Xiao .

Editor information

Editors and Affiliations

  1. University of Malaya, Kuala Lumpur, Malaysia

    Shivakumara Palaiahnakote

  2. Consiglio Nazionale delle Ricerche, ICAR, Naples, Italy

    Gabriella Sanniti di Baja

  3. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  4. Auckland University of Technology, Auckland, New Zealand

    Wei Qi Yan

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

Feng, Z., Du, C., Wang, Y., Xiao, B. (2020). OSTER: An Orientation Sensitive Scene Text Recognizer with CenterLine Rectification. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-41404-7_34

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41403-0

  • Online ISBN: 978-3-030-41404-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation