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Elegantly Written: Disentangling Writer and Character Styles for Enhancing Online Chinese Handwriting

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

The electronic writing tools, while enhancing convenience, sacrifice the readability and efficiency of handwritten content. Balancing high efficiency with readable handwriting poses a challenging research task. In this paper, we propose a method sequence-based models to beautify user handwritten traces. Unlike most existing methods that treat Chinese handwriting as images and cannot reflect the human writing process, we capture individual writing characteristics from a small amount of user handwriting trajectories and beautify the user’s traces by mimicking their writing style and process. We fully consider the style of radicals and components between the content and reference glyphs, assigning appropriate fine-grained styles to strokes in the content glyphs through a cross-attention mechanism module. Additionally, we find that many style features contribute minimally to the final stylized results. Therefore, we decompose the style features into the Cartesian product of single-dimensional variable sets, effectively removing redundant features with limited impact on the stylization effect while preserving key style information. Qualitative and quantitative experiments both demonstrate the superiority of our approach.

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Acknowledgements

This research was supported by Dalian Science and Technology Innovation Fund (No.2023JJGX026) and Key Laboratory of Informatization of National Education of Ministry of Education (No.EIN2024B002).

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Authors and Affiliations

  1. Faculty of Computer Science and Information Technology, University Putra Malaysia, 43400, UPM Serdang, Malaysia

    Yu Liu, Fatimah Binti Khalid, Lei Wang & Youxi Zhang

  2. Dalian Chinese Font Design Technology Innovation Center, Dalian Minzu University, 116600, Dalian, China

    Yu Liu & Cunrui Wang

Authors
  1. Yu Liu
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  2. Fatimah Binti Khalid
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  3. Lei Wang
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  4. Youxi Zhang
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  5. Cunrui Wang
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Corresponding author

Correspondence to Cunrui Wang .

Editor information

Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Hessen, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Liu, Y., Khalid, F.B., Wang, L., Zhang, Y., Wang, C. (2025). Elegantly Written: Disentangling Writer and Character Styles for Enhancing Online Chinese Handwriting. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15066. Springer, Cham. https://doi.org/10.1007/978-3-031-73242-3_23

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