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Style-consistency calligraphy synthesis system in digital library
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International Conference on Digital Libraries archive
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries table of contents
Austin, TX, USA
SESSION: 6: best paper nominees 2 table of contents
Pages 145-152  
Year of Publication: 2009
ISBN:978-1-60558-322-8
Authors
Kai Yu  Zhejiang University, Hangzhou, China
Jiangqin Wu  Zhejiang University, Hangzhou, China
Yueting Zhuang  Zhejiang University, Hangzhou, China
Sponsors
SIGIR: ACM Special Interest Group on Information Retrieval
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

There are lots of digitized calligraphy works written by ancient famous calligraphists in CADAL (China-America Digital Academic Library) digital library. To make use of these resources, users want to generate a tablet or a piece of calligraphic works written by some ancient famous calligraphist. But some characters in the tablet or the calligraphic work hadn't been written by the calligraphist or though were ever written but are hard to read because of long time weathering. In this paper, a novel approach is proposed to synthesize Chinese calligraphic characters which are in the same style of some calligraphist, and a corresponding system is developed for calligraphy works generation and tablets design.

Calligraphic character is represented by a three-level hierarchical model. A novel approach for determining the character structure is proposed, which takes advantage of both the structure of the same characters of different styles and the structure of similar characters of the same style. A style evaluation model (SEM) is presented to evaluate whether the calligraphic character generated is in the same style of the specified calligraphist and to adjust the calligraphic character generated. Our experiments show that this system is effective.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Kai Yu: colleagues
Jiangqin Wu: colleagues
Yueting Zhuang: colleagues