DocumentCode :
2022020
Title :
An EM Based Algorithm for Skew Detection
Author :
Egozi, A. ; Dinstein, Itshak
Author_Institution :
Ben-Gurion Univ. of the Negev, Beer-Sheva
Volume :
1
fYear :
2007
fDate :
23-26 Sept. 2007
Firstpage :
277
Lastpage :
281
Abstract :
We present a a statistical approach to skew detection, where the textual features of a document image are modeled as a mixture of straight lines in Gaussian noise. The EM algorithm is used to estimate the parameters of the mixture model and the skew angle estimate is extracted from the estimated parameters. Experiments prove that our method has some advantages over other existing methods in terms of accuracy and efficiency.
Keywords :
Gaussian noise; document image processing; expectation-maximisation algorithm; parameter estimation; Gaussian noise; document image textual feature; expectation maximization algorithm; parameter estimation; skew angle estimate; skew detection; Algorithm design and analysis; Character recognition; Clustering algorithms; Detection algorithms; Digital images; Gaussian noise; Least squares methods; Maximum likelihood estimation; Nearest neighbor searches; Parameter estimation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition, 2007. ICDAR 2007. Ninth International Conference on
Conference_Location :
Parana
ISSN :
1520-5363
Print_ISBN :
978-0-7695-2822-9
Type :
conf
DOI :
10.1109/ICDAR.2007.4378719
Filename :
4378719
Link To Document :
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