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Model-based Persian calligraphy synthesis via learning to transfer templates to personal styles

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Abstract

Current software tools for computer generation of Persian calligraphy can be mostly described as conventional fonts and typesetting software, which basically neglect the ‘variations’ of real calligraphy performed by hand, in terms of personalization to different calligraphers’ styles, as well as their statistical characteristics. In this paper, we address the problem of natural-looking Persian calligraphy synthesis via a machine learning based approach, at the level of subwords. Given images of samples written by a calligrapher, we train a parametric model to imitate the style. The core idea is to make use of templates (fonts) as a source of background knowledge, and learn a probabilistic mapping from them to personal styles of calligraphers, which is posed as transformation of attributed graphs using neural networks with sliding windows. This can be understood as adding ‘naturalness’ to a Persian calligraphy font, in essence. We report both objective and subjective evaluations, including the model performance in writer (calligrapher) identification task and Visual Turing Test. The results of the latter suggest that humans are unable to distinguish the calligraphy synthesized by our approach from real calligraphy in many cases.

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Authors and Affiliations

  1. Machine Learning and Computational Modeling Lab, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Amirhossein Ahmadian & Babak Nadjar Araabi

  2. Deep Learning Research Lab, Department of Computer Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Tehran, Iran

    Kazim Fouladi

Authors
  1. Amirhossein Ahmadian
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  2. Kazim Fouladi
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  3. Babak Nadjar Araabi
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Correspondence to Kazim Fouladi.

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A Appendix: Pseudocode for extracting segments

A Appendix: Pseudocode for extracting segments

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Ahmadian, A., Fouladi, K. & Araabi, B.N. Model-based Persian calligraphy synthesis via learning to transfer templates to personal styles. IJDAR 23, 183–203 (2020). https://doi.org/10.1007/s10032-020-00353-1

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