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K2 Algorithm-based Text Detection with An Adaptive Classifier Threshold
Khalid Iqbal, Xu-Cheng Yin, Hong-Wei Hao, Sohail Asghar, Hazrat Ali
Pages - 87 - 94     |    Revised - 10-05-2014     |    Published - 01-06-2014
Volume - 8   Issue - 3    |    Publication Date - June 2014  Table of Contents
Bayesian Network, Adaptive Threshold, Bayesian Logistic Regression, Scene Image.
In natural scene images, text detection is a challenging study area for dissimilar content-based image analysis tasks. In this paper, a Bayesian network scores are used to classify candidate character regions by computing posterior probabilities. The posterior probabilities are used to define an adaptive threshold to detect text in scene images with accuracy. Therefore, candidate character regions are extracted through maximally stable extremal region. K2 algorithm-based Bayesian network scores are learned by evaluating dependencies amongst features of a given candidate character region. Bayesian logistic regression classifier is trained to compute posterior probabilities to define an adaptive classifier threshold. The candidate character regions below from adaptive classifier threshold are discarded as non-character regions. Finally, text regions are detected with the use of effective text localization scheme based on geometric features. The entire system is evaluated on the ICDAR 2013 competition database. Experimental results produce competitive performance (precision, recall and harmonic mean) with the recently published algorithms.
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Dr. Khalid Iqbal
University of Science and Technology Beijing - China
Associate Professor Xu-Cheng Yin
University of Science and Technology Beijing - China
Professor Hong-Wei Hao
Chinese Academy of Sciences - China
Associate Professor Sohail Asghar
PMAS-Arid Agriculture University - Pakistan
Mr. Hazrat Ali
City University London - United Kingdom

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