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Robust and parallel Uyghur text localization in complex background images

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Abstract

Uyghur text localization in complex background images is a significant research for Uyghur image content analysis. In this paper, we propose a robust Uyghur text localization method in complex background images and provide a CPU–GPU heterogeneous parallelization scheme. Firstly, a multi-color-channel enhanced maximally stable extremal region is used to extract components in images, which is robust to blur and low contrast. Secondly, a two-stage component classification system is used to filter out non-text components. Finally, a component connected graph algorithm is proposed to construct text lines. Experiments on the proposed dataset demonstrate that our algorithm compares favorably with the state-of-the-art algorithms when handling Uyghur texts. Besides, the heterogeneous parallel implementation achieves 12.5 times speedup.

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Acknowledgements

This work is supported by the National Nature Science Foundation of China (61303171, 61303175), the “Strategic Priority Research Program” of the Chinese Academy of Sciences (XDA06031000) and Natural Science Foundation of Hunan Province (2016JJ2005).

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Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China

    Yun Song, Jianjun Chen & Xi Chen

  2. Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China

    Jianjun Chen & Hongtao Xie

  3. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhineng Chen

  4. Laboratory of Parallel Software and Computational Science, Institute of Software, Chinese Academy of Sciences, Beijing, China

    Xingyu Gao

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  1. Yun Song
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  2. Jianjun Chen
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  3. Hongtao Xie
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  4. Zhineng Chen
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  5. Xingyu Gao
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  6. Xi Chen
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Correspondence to Hongtao Xie.

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Song, Y., Chen, J., Xie, H. et al. Robust and parallel Uyghur text localization in complex background images. Machine Vision and Applications 28, 755–769 (2017). https://doi.org/10.1007/s00138-017-0837-3

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