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Manifold Learning and Its Application in Form Processing

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成果类型:
期刊论文、会议论文
作者:
He, Xiuling*;Yang, Yang;Chen, Zengzhao;Yu, Ying;Dong, Cailin
通讯作者:
He, Xiuling
作者机构:
[He, Xiuling; Chen, Zengzhao; Dong, Cailin; Yu, Ying] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.
[Yang, Yang] Univ Sci & Technol Beijing, Beijing 100083, Peoples R China.
[Yu, Ying] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
通讯机构:
[He, Xiuling] C
Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.
语种:
中文
关键词:
identification of character type;manifold learning;Locally Linear Embedding(LLE);parameter estimation
期刊:
Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
年:
2008
页码:
9286-9291
会议名称:
7th World Congress on Intelligent Control and Automation
会议时间:
JUN 25-27, 2008
会议地点:
Chongqing, PEOPLES R CHINA
会议主办单位:
[He, Xiuling;Chen, Zengzhao;Yu, Ying;Dong, Cailin] Cent China Normal Univ, Sch Math & Stat, Wuhan 430079, Peoples R China.^[Yang, Yang] Univ Sci & Technol Beijing, Beijing 100083, Peoples R China.^[Yu, Ying] Cent China Normal Univ, Dept Comp Sci, Wuhan 430079, Peoples R China.
会议赞助商:
Chongqing Univ, Chongqing Inst Technol, Chongqing Univ Sci & Technol, Xihua Univ, SW Univ Sci & Technol, IEEE Robot & Automat Soc, IEEE Control Syst Soc, Beijing Chapter, Chinese Assoc Automat, Chinese Assoc Artificial Intelligence, Natl Nat Sci Fdn, Chongqing Municipal Sci & Technol Comm, Chongqing Municipal Assoc Sci & Technol, KC Wong Educ Fdn
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
978-1-4244-2113-8
机构署名:
本校为第一且通讯机构
院系归属:
数学与统计学学院
计算机学院
摘要:
The identification of language and character type has been an active area of research after recognition of machine printed text. Research on identification of handwritten text and printed text is seldom conducted. But it is common used in recognition of form. For character type identification, manifold learning algorithm locally linear embedding (LLE) is imported. A generalizing method and a parameters estimation method are proposed. Experiments in identification printed/handwritten Chinese characters and digits show that its performance is higher than support vector machine (SVM) classificati...

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