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Global and Local Features Based Classification for Bleed-Through Removal

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Abstract

The text on one side of historical documents often seeps through and appears on the other side, so the bleed-through is a common problem in historical document images. It makes the document images hard to read and the text difficult to recognize. To improve the image quality and readability, the bleed-through has to be removed. This paper proposes a global and local features extraction based bleed-through removal method. The Gaussian mixture model is used to get the global features of the images. Local features are extracted by the patch around each pixel. Then, the extreme learning machine classifier is utilized to classify the scanned images into the foreground text and the bleed-through component. Experimental results on real document image datasets show that the proposed method outperforms the state-of-the-art bleed-through removal methods and preserves the text strokes well.

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Acknowledgments

This paper is supported by the National Natural Science Fund of China for Distinguished Young Scholars (No. 61325007) and the National Natural Science Fund of China for International Cooperation and Exchanges (No. 61520106001).

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Xiangyu Hu, Hui Lin, Shutao Li & Bin Sun

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  1. Xiangyu Hu
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  2. Hui Lin
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  3. Shutao Li
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Correspondence to Shutao Li.

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Hu, X., Lin, H., Li, S. et al. Global and Local Features Based Classification for Bleed-Through Removal. Sens Imaging 17, 9 (2016). https://doi.org/10.1007/s11220-016-0134-7

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