Paper Info
Title
Language independent skew estimation technique based on Gaussian mixture models: a case study on South Indian scripts
Abstract
During document scanning, skew is inevitably introduced into the incoming document image. Presence of additional modified characters, which get plugged in as extensions and remain as disjointed protrusions of a main character is really challenging in estimating inclination in skewed documents made up of texts in south Indian languages (Kannada, Telugu, Tamil and Malayalam). In this paper, we present a novel script independent (for south Indian) skew estimation technique based on Gaussian Mixture Models (GMM). The Expectation-Maximization (EM) algorithm is used to learn the mixture of Gaussians. Subsequently the cluster means are subjected to moments to estimate the skew angle. Experiments on printed and handwritten documents corrupted by noise is done. Our method shows significantly improved performance as compared to other existing methods.
Year
DOI
Venue
2007
10.1007/978-3-540-77046-6_60
PReMI
Keywords
Field
DocType
computer science,mixture of gaussians,expectation maximization,em algorithm,gaussian mixture model
Tamil,Kannada,Malayalam,Pattern recognition,Skew estimation,Computer science,Artificial intelligence,Skew,Mixture model,Telugu,Machine learning,Scripting language
Conference
Volume
ISSN
ISBN
4815
0302-9743
3-540-77045-3
Citations 
PageRank 
References 
2
0.39
11
Authors
3
Name
Order
Citations
PageRank
V. N. Manjunath Aradhya14116.69
Ashok Rao220019.14
G. Hemantha Kumar322227.92