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Robust Text Detection in Natural Scene Images

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AI 2016: Advances in Artificial Intelligence (AI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9992))

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Abstract

Natural scenes of blurred text images are challenges to current text recognition field. In our paper, a novel method for text detection in natural scene image is suggested using edge detection, maximally stable extremal region (MSER) and tensor voting. Edge detection and MSER methods are combined to find the greatest character candidates from stable areas which are extracted from an input image. These text candidates are used to extract the text line information using tensor voting that creates normal vectors and curve saliency values in characters along the text lines. Therefore, the text line information is used to eliminate non-text areas. Our method is evaluated on the ICDAR2013 datasets and experiment results show that the proposed result is compared to the previous methods.

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References

  1. Jiang, R., Qi, F., Xu, L., Wu, G.: Using connected components’ features to detect and segment text. J. Image Graph. 11, 1653–1656 (2006)

    Google Scholar 

  2. Zhang, H., Liu, C., Yang, C., Ding, X., Wang, K.Q.: An improved scene text extraction method using conditional random field and optical character recognition. In: International Conference on Document Analysis and Recognition, ICDAR, pp. 708–712 (2011)

    Google Scholar 

  3. Liu, X., Samarabandu, J.: Multiscale edge-based text extraction from complex images. In: IEEE International Conference on Multimedia and Expo, ICME (2006)

    Google Scholar 

  4. Ye, Q., Jiao, J., Huang, J., Yu, H.: Text detection and restoration in natural scene images. J. Vis. Commun. Image Represent. 18, 504–513 (2007)

    Article  Google Scholar 

  5. Ezaki, N., Bulacu, M., Schomaker, L.: Text detection from natural scene images: towards a system for visually impaired persons. In: Proceedings of the 17th International Conference on Pattern Recognition, ICPR, vol. 2, pp. 683–686 (2004)

    Google Scholar 

  6. Zhou, G., Liu, Y., Meng, Q., Zhang, Y.: Detecting multi lingual text in natural scene. In: International Symposium on Access Spaces, ISAS, pp. 116–120 (2011)

    Google Scholar 

  7. Hanif, S.M., Prevost, L.: Text detection and localization in complex scene images using constrained adaboost algorithm. In: 10th International Conference on Document Analysis and Recognition, ICDAR, pp. 1–5 (2009)

    Google Scholar 

  8. Angadi, S.A., Kodabagi, M.M.: A texture based methodology for text region extraction from low resolution natural scene images. In: Advance Computing Conference, pp. 121–128 (2010)

    Google Scholar 

  9. Pan, Y.F., Zhu, Y., Sun, J., Naoi, S.: Improving scene text detection by scale adaptive segmentation and weighted CRF verification. In: International Conference on Document Analysis and Recognition, ICDAR, pp. 759–763 (2011)

    Google Scholar 

  10. Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 2963–2970 (2010)

    Google Scholar 

  11. Yi, C., Tian, Y.L.: Text string detection from natural scenes by structure based partition and grouping. IEEE Trans. Image Proc. 20, 2594–2605 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  12. Gonzalez, A., et al.: Text location in complex images. In: IEEE 21st International Conference on Pattern Recognition, ICPR, pp. 617–620 (2012)

    Google Scholar 

  13. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Image net classification with deep convolutional neural networks. In: Proceedings of NIPS, pp. 1106–1114 (2012)

    Google Scholar 

  14. Huang, W., Qiao, Y., Tang, X.: Robust scene text detection with convolution neural network induced MSER trees. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part IV. LNCS, vol. 8692, pp. 497–511. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10593-2_33

    Chapter  Google Scholar 

  15. Jaderberg, M., Vedaldi, A., Zisserman, A.: Deep Features for Text Spotting. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part IV. LNCS, vol. 8692, pp. 512–528. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10593-2_34

    Chapter  Google Scholar 

  16. Nguyen, T.D., Park, J., Lee, G.: Tensor voting based text localization in natural scene images. IEEE Sig. Process. Lett. 17, 639–642 (2010)

    Article  Google Scholar 

  17. Shi, C., Wang, C., Xiao, B., Zhang, Y., Gao, S.: Scene text detection using graph model built upon maximally stable extremal regions. Pattern Recogn. Lett. 34, 107–116 (2013)

    Article  Google Scholar 

  18. Yin, X.-C., Huang, K., Hao, H.-W.: Robust text detection in natural scene images. IEEE Trans. Pattern Anal. Mach. Intell. PAMI 36, 970–983 (2013)

    Google Scholar 

  19. Chen, H., et al.: Robust text detection in natural images with edge-enhanced maximally stable extremal regions. In: 18th IEEE International Conference on Image Processing, ICIP (2011)

    Google Scholar 

  20. Li, Y., Lu, H.: Scene text detection via stroke width. In: IEEE 21st International Conference on Pattern Recognition, ICPR, pp. 681–684 (2012)

    Google Scholar 

  21. Nguyen, T.D., Lee, G.: Color image segmentation using tensor voting based color clustering. Pattern Recogn. Lett. 33, 605–614 (2012)

    Article  Google Scholar 

Download references

Acknowledgement

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by MEST (NRF- 2015R1D1A1A01060172).

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Authors and Affiliations

  1. Department of Electronics and Computer Engineering, Chonnam National University, Kwangju, South Korea

    Van Khien Pham & GueeSang Lee

Authors
  1. Van Khien Pham
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  2. GueeSang Lee
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Corresponding author

Correspondence to GueeSang Lee .

Editor information

Editors and Affiliations

  1. University of Tasmania, Hobart, Australia

    Byeong Ho Kang

  2. Auckland University of Technology, Auckland, New Zealand

    Quan Bai

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Pham, V.K., Lee, G. (2016). Robust Text Detection in Natural Scene Images. In: Kang, B.H., Bai, Q. (eds) AI 2016: Advances in Artificial Intelligence. AI 2016. Lecture Notes in Computer Science(), vol 9992. Springer, Cham. https://doi.org/10.1007/978-3-319-50127-7_66

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  • DOI: https://doi.org/10.1007/978-3-319-50127-7_66

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50126-0

  • Online ISBN: 978-3-319-50127-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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