Abstract
Text detection in natural images has been a high demand for a lot real-life applications such as image retrieval and self-navigation. This work deals with the problem of robust text detection especially for multi-script in natural scene images. Unlike the existing works that consider multi-script characters as groups of text fragments, we consider them as non-connected components. Specifically, we firstly propose a novel representation named Linked Extremal Regions (LER) to extract full characters instead of fragments of scene characters. Secondly, we propose a two-stage convolution neural networks for discriminating multi-script texts in clutter background images for more robust text detection. Experimental results on three well-known datasets, namely, ICDAR 2011, 2013 and MSRA-TD500, demonstrate that the proposed method outperforms the state-of-the-art methods, and is also language independent.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: British Machine Vision Conference, pp. 384–393 (2002)
Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2963–2970 (2010)
Neumann, L., Matas, J.: Real-time scene text localization and recognition. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3538–3545 (2012)
Neumann, L., Matas, J.: Scene text localization and recognition with oriented stroke detection. In: IEEE International Conference on Computer Vision, pp. 97–104 (2013)
Huang, W., Qiao, Yu., 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. LNCS, vol. 8692, pp. 497–511. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10593-2_33
Xu, H., Su, F.: A robust hierarchical detection method for scene text based on convolutional neural networks. In: IEEE International Conference on Multimedia and Expo, pp. 1–6 (2015)
Sung, M.C., Jun, B., Cho, H., Kim, D.: Scene text detection with robust character candidate extraction method. In: International Conference on Document Analysis and Recognition, pp. 426–430 (2015)
Zhang, Z., Shen, W., Yao, C., Bai, X.: Symmetry-based text line detection in natural scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4321–4329 (2015)
Yin, X.C., Yin, X., Huang, K., Hao, H.: Robust text detection in natural scene images. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 36(5), 970–983 (2014)
Yin, X.C., Pei, W.Y., Zhang, J., Hao, H.: Multi-orientation scene text detection with adaptive clustering. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 37(9), 1930–1937 (2015)
Yao, C., Bai, X., Liu, W., Tu, Z.: Detecting texts of arbitrary orientations in natural images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1083–1090 (2012)
Kang, L., Li, Y., Doermann, D.: Orientation robust text line detection in natural images. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 4034–4041 (2014)
Ren, S., Cao, X., Wei, Y., Sun, J.: Face alignment at 3000 FPS via regressing local binary features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1685–1692 (2014)
Acknowledgments
The work described in this paper was supported by the Natural Science Foundation of China under Grant Nos. 61672273, 61272218 and 61321491, the Science Foundation for Distinguished Young Scholars of Jiangsu under Grant No. BK20160021.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Liu, RZ. et al. (2017). Robust Scene Text Detection for Multi-script Languages Using Deep Learning. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10132. Springer, Cham. https://doi.org/10.1007/978-3-319-51811-4_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-51811-4_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51810-7
Online ISBN: 978-3-319-51811-4
eBook Packages: Computer ScienceComputer Science (R0)