TY - JOUR
T1 - Statistical mixture model for documents skew angle estimation
AU - Egozi, Amir
AU - Dinstein, Its'Hak
N1 - Funding Information:
This work was partially supported by the Paul Ivanier Center for Robotics and Production Management at Ben-Gurion University, Israel, and in part by ISF Grant 1266/09 , “Algorithms for historical document analysis in Hebrew and Arabic”.
PY - 2011/10/15
Y1 - 2011/10/15
N2 - We present a statistical approach to skew detection, where the distribution of textual features of document images is modeled as a mixture of straight lines in Gaussian noise. The Expectation Maximization (EM) algorithm is used to estimate the parameters of the statistical model and the estimated skew angle is extracted from the estimated parameters. Experiments demonstrate that our method is favorably comparable to other existing methods in terms of accuracy and efficiency.
AB - We present a statistical approach to skew detection, where the distribution of textual features of document images is modeled as a mixture of straight lines in Gaussian noise. The Expectation Maximization (EM) algorithm is used to estimate the parameters of the statistical model and the estimated skew angle is extracted from the estimated parameters. Experiments demonstrate that our method is favorably comparable to other existing methods in terms of accuracy and efficiency.
KW - Expectation Maximization (EM)
KW - Linear least squares
KW - Skew detection
KW - Statistical mixture models
UR - http://www.scopus.com/inward/record.url?scp=80052842603&partnerID=8YFLogxK
U2 - 10.1016/j.patrec.2011.07.004
DO - 10.1016/j.patrec.2011.07.004
M3 - Article
AN - SCOPUS:80052842603
SN - 0167-8655
VL - 32
SP - 1912
EP - 1921
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
IS - 14
ER -