• Login
    View Item 
    •   Eurographics DL Home
    • Computer Graphics Forum
    • Volume 38 (2019)
    • 38-Issue 7
    • View Item
    •   Eurographics DL Home
    • Computer Graphics Forum
    • Volume 38 (2019)
    • 38-Issue 7
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    FontRNN: Generating Large-scale Chinese Fonts via Recurrent Neural Network

    Thumbnail
    View/Open
    v38i7pp567-577.pdf (6.219Mb)
    4-3_chinese_character_generation_via_rnn.pdf (1.012Mb)
    4-4_shape_synthesis.pdf (7.159Mb)
    4-5_handwriting_generation.pdf (2.416Mb)
    writing_robot.mp4 (3.023Mb)
    Date
    2019
    Author
    Tang, Shusen ORCID
    Xia, Zeqing ORCID
    Lian, Zhouhui ORCID
    Tang, Yingmin
    Xiao, Jianguo
    Pay-Per-View via TIB Hannover:

    Try if this item/paper is available.

    Metadata
    Show full item record
    Abstract
    Despite the recent impressive development of deep neural networks, using deep learning based methods to generate largescale Chinese fonts is still a rather challenging task due to the huge number of intricate Chinese glyphs, e.g., the official standard Chinese charset GB18030-2000 consists of 27,533 Chinese characters. Until now, most existing models for this task adopt Convolutional Neural Networks (CNNs) to generate bitmap images of Chinese characters due to CNN based models' remarkable success in various applications. However, CNN based models focus more on image-level features while usually ignore stroke order information when writing characters. Instead, we treat Chinese characters as sequences of points (i.e., writing trajectories) and propose to handle this task via an effective Recurrent Neural Network (RNN) model with monotonic attention mechanism, which can learn from as few as hundreds of training samples and then synthesize glyphs for remaining thousands of characters in the same style. Experimental results show that our proposed FontRNN can be used for synthesizing large-scale Chinese fonts as well as generating realistic Chinese handwritings efficiently.
    BibTeX
    @article {10.1111:cgf.13861,
    journal = {Computer Graphics Forum},
    title = {{FontRNN: Generating Large-scale Chinese Fonts via Recurrent Neural Network}},
    author = {Tang, Shusen and Xia, Zeqing and Lian, Zhouhui and Tang, Yingmin and Xiao, Jianguo},
    year = {2019},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    ISSN = {1467-8659},
    DOI = {10.1111/cgf.13861}
    }
    URI
    https://doi.org/10.1111/cgf.13861
    https://diglib.eg.org:443/handle/10.1111/cgf13861
    Collections
    • 38-Issue 7

    Eurographics Association copyright © 2013 - 2022 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA
     

     

    Browse

    All of Eurographics DLCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister

    BibTeX | TOC

    Create BibTeX Create Table of Contents

    Eurographics Association copyright © 2013 - 2022 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA