Paper Info
Title
Nonlinear model and constrained ML for removing back-to-front interferences from recto-verso documents
Abstract
In this paper, we approach the removal of back-to-front interferences from scans of double-sided documents as a blind source separation problem, and extend our previous linear mixing model to a more effective nonlinear mixing model. We consider the front and back ideal images as two individual patterns overlapped in the observed recto and verso scans, and apply an unsupervised constrained maximum likelihood technique to separate them. Through several real examples, we show that the results obtained by this approach are much better than the ones obtained through data decorrelation or independent component analysis. As compared to approaches based on segmentation/classification, which often aim at cleaning a foreground text by removing all the textured background, one of the advantages of our method is that cleaning does not alter genuine features of the document, such as color or other structures it may contain. This is particularly interesting when the document has a historical importance, since its readability can be improved while maintaining the original appearance.
Year
DOI
Venue
2012
10.1016/j.patcog.2011.07.016
Pattern Recognition
Keywords
Field
DocType
recto-verso document,double-sided document,independent component analysis,historical importance,back-to-front interference,nonlinear model,effective nonlinear,genuine feature,foreground text,ideal image,data decorrelation,blind source separation problem
Nonlinear system,Decorrelation,Pattern recognition,Computer science,Segmentation,Maximum likelihood,Readability,Artificial intelligence,Independent component analysis,Blind signal separation,Nonlinear model,Machine learning
Journal
Volume
Issue
ISSN
45
1
0031-3203
Citations 
PageRank 
References 
7
0.54
14
Authors
4
Name
Order
Citations
PageRank
Francesca Martinelli1313.36
Emanuele Salerno225029.21
Ivan Gerace3396.55
Anna Tonazzini438239.07