NDLI: Text Localization in Web Images Using Probabilistic Candidate Selection Model

Content Provider	IEEE Xplore Digital Library
Author	Liangji Situ Ruizhe Liu Chew Lim Tan
Copyright Year	2011
Abstract	Web has become increasingly oriented to multimedia content. Most information on the web is conveyed from images. Text localization in web image plays an important role in web image information extraction and retrieval. Current works on text localization in web images assume that text regions are in homogenous color and high contrast. Hence, the approaches may fail when text regions are in multi-color or imposed in complex background. In this paper, we propose a text extraction algorithm from web images based on the probabilistic candidate selection model. The model firstly segments text region candidates from input images using wavelet, Gaussian mixture model (GMM) and triangulation. The likelihood of a candidate region containing text is then learnt using a Bayesian probabilistic model from two features, namely, histogram of oriented gradient (HOG) and local binary pattern histogram Fourier feature (LBP-HF). Finally best candidate regions are integrated to form text regions. The algorithm is evaluated using 155 non-homogenous web images containing around 600 text regions. The results show that the proposed model is able to extract text regions from non-homogenous images effectively.
Starting Page	1359
Ending Page	1363
File Size	855730
Page Count	5
File Format	PDF
ISBN	9781457713507
ISSN	15205363
e-ISBN	9780769545202
DOI	10.1109/ICDAR.2011.273
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Feature extraction Image segmentation Probabilistic logic Image color analysis Computational modeling Data mining web image text extraction text localization
Content Type	Text
Resource Type	Article

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