DocumentCode
2502574
Title
Manifold learning and its application in form processing
Author
He, Xiuling ; Yang, Yang ; Chen, Zengzhao ; Yu, Ying ; Dong, Cailin
Author_Institution
Sch. of Math. & Stat., Central China Normal Univ., Wuhan
fYear
2008
fDate
25-27 June 2008
Firstpage
9292
Lastpage
9296
Abstract
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) classification. The combination of dimensionality reduction of LLE and linear discriminant analysis (LDA) classification achieves a similar accurate rate as combination of LLE and SVM classification but runs much faster than it.
Keywords
business forms; handwritten character recognition; image classification; learning (artificial intelligence); parameter estimation; support vector machines; text analysis; character type identification; form processing; handwritten text identification; linear discriminant analysis; locally linear embedding; machine printed text; manifold learning; parameter estimation method; support vector machine classification; Application software; Automation; Intelligent control; Linear discriminant analysis; Machine learning; Manifolds; Mathematics; Parameter estimation; Support vector machine classification; Support vector machines; Locally Linear Embedding(LLE); identification of character type; manifold learning; parameter estimation;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location
Chongqing
Print_ISBN
978-1-4244-2113-8
Electronic_ISBN
978-1-4244-2114-5
Type
conf
DOI
10.1109/WCICA.2008.4594402
Filename
4594402
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