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CLC number: TP391.4

On-line Access: 2011-11-04

Received: 2011-01-02

Revision Accepted: 2011-07-13

Crosschecked: 2011-09-28

Cited: 4

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.11 P.873-884


Efficient shape matching for Chinese calligraphic character retrieval

Author(s):  Wei-ming Lu, Jiang-qin Wu, Bao-gang Wei, Yue-ting Zhuang

Affiliation(s):  School of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   luwm@zju.edu.cn, wujq@zju.edu.cn, wbg@zju.edu.cn, yzhuang@zju.edu.cn

Key Words:  Calligraphy, Shape feature, Character retrieval, Efficient matching

Wei-ming Lu, Jiang-qin Wu, Bao-gang Wei, Yue-ting Zhuang. Efficient shape matching for Chinese calligraphic character retrieval[J]. Journal of Zhejiang University Science C, 2011, 12(11): 873-884.

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author="Wei-ming Lu, Jiang-qin Wu, Bao-gang Wei, Yue-ting Zhuang",
journal="Journal of Zhejiang University Science C",
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%T Efficient shape matching for Chinese calligraphic character retrieval
%A Wei-ming Lu
%A Jiang-qin Wu
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%A Yue-ting Zhuang
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%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100005

T1 - Efficient shape matching for Chinese calligraphic character retrieval
A1 - Wei-ming Lu
A1 - Jiang-qin Wu
A1 - Bao-gang Wei
A1 - Yue-ting Zhuang
J0 - Journal of Zhejiang University Science C
VL - 12
IS - 11
SP - 873
EP - 884
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1100005

An efficient search method is desired for calligraphic characters due to the explosive growth of calligraphy works in digital libraries. However, traditional optical character recognition (OCR) and handwritten character recognition (HCR) technologies are not suitable for calligraphic character retrieval. In this paper, a novel shape descriptor called SC-HoG is proposed by integrating global and local features for more discriminability, where a gradient descent algorithm is used to learn the optimal combining parameter. Then two efficient methods, keypoint-based method and locality sensitive hashing (LSH) based method, are proposed to accelerate the retrieval by reducing the feature set and converting the feature set to a feature vector. Finally, a re-ranking method is described for practicability. The approach filters query-dissimilar characters using the LSH-based method to obtain candidates first, and then re-ranks the candidates using the keypoint- or sample-based method. Experimental results demonstrate that our approaches are effective and efficient for calligraphic character retrieval.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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