DocumentCode :
2827047
Title :
A robust skew detection method based on Maximum Gradient Difference and R-signature
Author :
Felhi, Mehdi ; Bonnier, Nicolas ; Tabbone, Salvatore
fYear :
2011
fDate :
11-14 Sept. 2011
Firstpage :
2617
Lastpage :
2620
Abstract :
In this paper we study the detection of skewed text lines in scanned document images. The aim of our work is to develop a new automatic approach able to estimate precisely the skew angle of text in document images. Our new method is based on Maximum Gradient Difference (MGD) and R-signature. It detects zones that have high variations of gray values in different directions using the MGD transform. We consider these zones as being text regions. R-signature which is a shape descriptor based on Radon transform is then applied in order to approximate the skew angle. The accuracy of the proposed algorithm is evaluated on an open dataset by comparing error rates.
Keywords :
Radon transforms; document image processing; gradient methods; R-signature; Radon transform; maximum gradient difference; scanned document images; shape descriptor; skew detection method; skewed text line detection; Conferences; Image segmentation; Layout; Robustness; Shape; Transforms; R-signature; Skew detection; document image analysis; maximum gradient difference; text detection;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2011 18th IEEE International Conference on
Conference_Location :
Brussels
ISSN :
1522-4880
Print_ISBN :
978-1-4577-1304-0
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2011.6116202
Filename :
6116202
Link To Document :
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