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Fast document image comparison in multilingual corpus without OCR

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Abstract

This paper proposes a method to compare document images in multilingual corpus, which is composed of character segmentation, feature extraction and similarity measure. In character segmentation, a top-down strategy is used. We apply projection and self-adaptive threshold to analyze the layout and then segment the text line by horizontal projection. Then, English, Chinese and Japanese are recognized by different methods based on the distribution and ratios of text line. Finally, character segmentation with different languages is done using different strategies. In feature extraction and similarity measure, four features are given for coarse measurement, and then a template is set up. Based on the templates, a fast template matching method based on coarse-to-fine strategy and bit memory is presented for precise matching. The experimental results demonstrate that our method can handle multilingual document images of different resolutions and font sizes with high precision and speed.

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Acknowledgments

This work was supported by Shaanxi Social Science Foundation of China under Grant Nos. 13K093 and 2015K014, National Social Science Foundation of China under Grant No. 12BYY055, Shaanxi Twelfth Year Planning Foundation of China under Grant No. SGH13015 and the Fundamental Research Funds for the Central Universities under Grant No. Sk2013008.

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Authors and Affiliations

  1. School of Foreign Studies, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China

    Yuping Lin, Yingyu Li & Fang Wang

  2. School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an, 710049, Shaanxi, China

    Yonghong Song

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  1. Yuping Lin
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  2. Yingyu Li
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  4. Fang Wang
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Correspondence to Yonghong Song.

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Lin, Y., Li, Y., Song, Y. et al. Fast document image comparison in multilingual corpus without OCR. Multimedia Systems 23, 315–324 (2017). https://doi.org/10.1007/s00530-015-0484-3

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