Paper Info
Title
An ICA Based Approach for Complex Color Scene Text Binarization
Abstract
Binarization of text in natural scene images is a challenging task due to the variations in color, size, and font of the text and the results are often affected by complex backgrounds, different lighting conditions, shadows and reflections. A robust solution to this problem can significantly enhance the accuracy of scene text recognition algorithms leading to a variety of applications such as scene understanding, automatic localization and navigation, and image retrieval. In this paper, we propose a method to extract and binarize text from images that contains complex background. We use an Independent Component Analysis (ICA) based technique to map out the text region, which is inherently uniform in nature, while removing shadows, specularity and reflections, which are included in the background. The technique identifies the text regions from the components extracted by ICA using a global thresholding method to isolate the foreground text. We show the results of our algorithm on some of the most complex word images from the ICDAR 2003 Robust Word Recognition Dataset and compare with previously reported methods.
Year
DOI
Venue
2013
10.1109/ACPR.2013.16
ACPR
Keywords
Field
DocType
scene understanding,independent component analysis,complex word image,complex background,text region,global thresholding method,natural scene image,foreground text,binarize text,complex color scene text,scene text recognition,image segmentation,feature extraction
Computer vision,Specularity,Pattern recognition,Computer science,Font,Word recognition,Image retrieval,Image segmentation,Feature extraction,Independent component analysis,Artificial intelligence,Thresholding
Conference
ISSN
Citations 
PageRank 
0730-6512
0
0.34
References 
Authors
9
2
Name
Order
Citations
PageRank
Siddharth Kherada100.34
Anoop M. Namboodiri225526.36