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Classifying fonts and calligraphy styles using complex wavelet transform

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Abstract

Recognizing fonts has become an important task in document analysis, due to the increasing number of available digital documents in different fonts and emphases. A generic font recognition system independent of language, script and content is desirable for processing various types of documents. At the same time, categorizing calligraphy styles in handwritten manuscripts is important for paleographic analysis, but has not been studied sufficiently in the literature. We address the font recognition problem as analysis and categorization of textures. We extract features using complex wavelet transform and use support vector machines for classification. Extensive experimental evaluations on different datasets in four languages and comparisons with state-of-the-art studies show that our proposed method achieves higher recognition accuracy while being computationally simpler. Furthermore, on a new dataset generated from Ottoman manuscripts, we show that the proposed method can also be used for categorizing Ottoman calligraphy with high accuracy.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Northeastern University, 360 Huntington avenue, Boston, MA, 02115, USA

    Alican Bozkurt

  2. Department of Computer Science, Hacettepe University, Beytepe Campus, 06800, Ankara, Turkey

    Pinar Duygulu

  3. Department of Electrical and Electronics Engineering, Bilkent University, 06800, Bilkent, Ankara, Turkey

    A. Enis Cetin

Authors
  1. Alican Bozkurt
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  2. Pinar Duygulu
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  3. A. Enis Cetin
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Correspondence to Alican Bozkurt.

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Bozkurt, A., Duygulu, P. & Cetin, A.E. Classifying fonts and calligraphy styles using complex wavelet transform. SIViP 9 (Suppl 1), 225–234 (2015). https://doi.org/10.1007/s11760-015-0795-z

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  • DOI: https://doi.org/10.1007/s11760-015-0795-z

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