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Optical Font Recognition of Chinese Characters Based on Texture Features

  • Conference paper
Advances in Machine Learning and Cybernetics

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3930))

Abstract

Font recognition is a fundamental issue in the identification, analysis and reconstruction of documents. In this paper, a new method of optical font recognition is proposed which could recognize the font of every Chinese character. It employs a statistical method based on global texture analysis to recognize a predominant font, and uses a traditional recognizer of a single font to identify the font of a single character by the guidance of an obtained predominant font. It consists of three steps. First, the guiding fonts are acquired based on Gabor features. Then a font recognizer is run to identify the font of the characters one by one. Finally, a post-processing is fulfilled according to the layout knowledge to correct the errors of font recognition. Experiments are carried out and the results show that this method is of immense practical and theoretical value.

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Author information

Authors and Affiliations

  1. College of Physics Science and Technology, Hebei University, 071002, Baoding, Hebei, China

    Ming-hu Ha & Xue-dong Tian

Authors
  1. Ming-hu Ha
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  2. Xue-dong Tian
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Editor information

Editors and Affiliations

  1. Department of Computing, Hong Kong Polytechnic University, P.O. Box, Hong Kong, China

    Daniel S. Yeung

  2. School of Creative Media, City University of Hong Kong,, China

    Zhi-Qiang Liu

  3. Department of Mathematics and Computer Science, Hebei University, 071002, Baoding, Hebei, P.R. China

    Xi-Zhao Wang

  4. School of Electrical and Information Engineering, University of Sydney, 2006, NSW, Australia

    Hong Yan

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© 2006 Springer-Verlag Berlin Heidelberg

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Ha, Mh., Tian, Xd. (2006). Optical Font Recognition of Chinese Characters Based on Texture Features. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_105

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  • DOI: https://doi.org/10.1007/11739685_105

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33584-9

  • Online ISBN: 978-3-540-33585-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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