Abstract
Text detection in complex background images is an important prerequisite for many image content analysis tasks. Actually, nearly all the widely-used methods of text detection focus on English and Chinese while some minority languages, such as Uyghur language, are paid less attention by researchers. In this paper, we propose a system which detects Uyghur language text in complex background images. First, component candidates are detected by the channel-enhanced Maximally Stable Extremal Regions (MSERs) algorithm. Then, most non-text regions are removed by a two-layer filtering mechanism. Next, the remaining component regions are connected into short chains, and the short chains are expanded by an expansion algorithm to connect the missed MSERs. Finally, the chains are identified by a Random Forest classifier. Experimental comparisons on the proposed dataset prove that our algorithm is effective for detecting Uyghur language text in complex background images. The F-measure is 84.8%, much better than the state-of-the-art performance of 75.5%.
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References
Xie, H., Zhang, Y., Tan, J., Guo, L., Li, J.: Contextual query expansion for image retrieval. IEEE Trans. Multimedia 16(4), 1104–1114 (2014)
Nie, L., Yan, S., Wang, M., Hong, R., Chua, T.S.: Harvesting visual concepts for image search with complex queries. In: ACM MM (2012)
Yin, X.C., Yin, X., et al.: Robust text detection in natural scene images. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 970–983 (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
Neumann, L., Matas, J.: Real-time scene text localization and recognition. In: IEEE CVPR, vol. 157, no. 10, pp. 3538–3545 (2012)
Yao, C., et al.: Detecting texts of arbitrary orientations in natural images. In: IEEE CVPR, pp. 1083–1090 (2012)
Zhang, Z., Shen, W., Yao, C., Bai, X.: Symmetry-based text line detection in natural scenes. In: IEEE CVPR, Boston, MA, June 2015
Xie, H., Gao, K., Zhang, Y., Tang, S., Li, J., Liu, Y.: Efficient feature detection and effective post-verification for large scale near-duplicate image search. IEEE Trans. Multimedia 13(6), 1319–1332 (2011)
Matas, J., et al.: Robust wide-baseline stereo from maximally stable extremal regions. IVC 2(10), 761–767 (2002)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE CVPR, vol. 1, pp. 886–893 (2005)
Leo, B.: Random forests. Mach. Learn. 45(6), 422–432 (2001)
Wang, T., Wu, D.J., Coates, A., et al.: End-to-end text recognition with convolutional neural networks. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3304–3308. IEEE (2012)
Chen, Z., Feng, B., Xie, H., Zheng, R., Xu, B.: Video to article hyperlinking by multiple tag property exploration. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014. LNCS, vol. 8325, pp. 62–73. Springer, Heidelberg (2014). doi:10.1007/978-3-319-04114-8_6
Chen, Z., Chen, Y., Gao, X., et al.: Unobtrusive sensing incremental social contexts using fuzzy class incremental learning. In: International Conference on Data Mining (2015)
Gao, X., Chen, Z., Tang, S., Zhang, Y., Li, J.: Adaptive weighted imbalance learning with application to abnormal activity recognition. Neurocomputing 173, 1927–1935 (2016)
Acknowledgement
This work is supported by the National Nature Science Foundation of China (61303171, 61502477, 61502479), the “Strategic Priority Research Program” of the Chinese Academy of Sciences (XDA06031000).
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Liu, S., Xie, H., Zhou, C., Mao, Z. (2017). Uyghur Language Text Detection in Complex Background Images Using Enhanced MSERs. 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_40
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DOI: https://doi.org/10.1007/978-3-319-51811-4_40
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