W-Net: One-shot arbitrary-style chinese character generation with deep neural networks

Haochuan Jiang, Guanyu Yang, Kaizhu Huang*, Rui Zhang

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

15 Citations (Scopus)


Due to the huge category number, the sophisticated combinations of various strokes and radicals, and the free writing or printing styles, generating Chinese characters with diverse styles is always considered as a difficult task. In this paper, an efficient and generalized deep framework, namely, the W-Net, is introduced for the one-shot arbitrary-style Chinese character generation task. Specifically, given a single character (one-shot) with a specific style (e.g., a printed font or hand-writing style), the proposed W-Net model is capable of learning and generating any arbitrary characters sharing the style similar to the given single character. Such appealing property was rarely seen in the literature. We have compared the proposed W-Net framework to many other competitive methods. Experimental results showed the proposed method is significantly superior in the one-shot setting.

Original languageEnglish
Title of host publicationNeural Information Processing - 25th International Conference, ICONIP 2018, Proceedings
EditorsAndrew Chi Sing Leung, Long Cheng, Seiichi Ozawa
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783030042202
Publication statusPublished - 2018
Event25th International Conference on Neural Information Processing, ICONIP 2018 - Siem Reap, Cambodia
Duration: 13 Dec 201816 Dec 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11305 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th International Conference on Neural Information Processing, ICONIP 2018
CitySiem Reap

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