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Automatic filter selection using image quality assessment


Automatic filter selection using image quality assessment

De Souza, Andrea Barretto (2003) Automatic filter selection using image quality assessment. Masters thesis, Concordia University.



We present a method for automatically selecting the best filter to treat poorly printed documents using image quality assessment. In order to estimate the quality of the image, we introduce five quality measures: stroke thickness factor, broken character factor, touching character factor, small speckle factor, and white speckle factor. Based on the information provided by the quality measures, a set of rules uses a two-stage decision process to choose the best filter among 4 morphological filters to be applied to an image. Other preprocessing tasks implemented are: skew correction, connected components analysis, and detection of reference lines. Our database contains 736 document images that were divided in three sets: training, validation and testing. Most images have one or more of the following degradations: broken characters, touching characters and salt-and-pepper noise. A training set of 370 images was used to develop the system. Experimental results on the test set of 183 images show a significant improvement in the recognition rate from 73.24% using no filter at all to 93.09% after applying a filter that was automatically selected. The recognition rate refers to the number of characters that were correctly recognized in the image using a commercial OCR. Three commercial OCR's were used to demonstrate the improvement obtained in the recognition rates in the training set.

Divisions:Concordia University > Faculty of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (Masters)
Authors:De Souza, Andrea Barretto
Pagination:x, 85 leaves : ill., tables ; 29 cm.
Institution:Concordia University
Degree Name:Theses (M.Comp.Sc.)
Program:Computer Science and Software Engineering
Thesis Supervisor(s):Suen, Ching Y
ID Code:2329
Deposited By: Concordia University Libraries
Deposited On:27 Aug 2009 17:27
Last Modified:08 Dec 2010 15:26
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