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Detection of Text Candidate Regions using Region Information-based Genetic Algorithm  

Oh, Jun-Taek (School of Electrical Engineering and Computer Science, Yeungnam University)
Kim, Wook-Hyun (School of Electrical Engineering and Computer Science, Yeungnam University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.45, no.6, 2008 , pp. 70-77 More about this Journal
Abstract
This paper proposes a new text candidate region detection method that uses genetic algorithm based on information of the segmented regions. In image segmentation, a classification of the pixels at each color channel and a reclassification of the region-unit for reducing inhomogeneous clusters are performed. EWFCM(Entropy-based Weighted C-Means) algorithm to classify the pixels at each color channel is an improved FCM algorithm added with spatial information, and therefore it removes the meaningless regions like noise. A region-based reclassification based on a similarity between each segmented region of the most inhomogeneous cluster and the other clusters reduces the inhomogeneous clusters more efficiently than pixel- and cluster-based reclassifications. And detecting text candidate regions is performed by genetic algorithm based on energy and variance of the directional edge components, the number, and a size of the segmented regions. The region information-based detection method can singles out semantic text candidate regions more accurately than pixel-based detection method and the detection results will be more useful in recognizing the text regions hereafter. Experiments showed the results of the segmentation and the detection. And it confirmed that the proposed method was superior to the existing methods.
Keywords
Text detection; EWFCM; Genetic algorithm; Image segmentation;
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