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http://dx.doi.org/10.3745/KIPSTB.2005.12B.7.737

Three-Level Color Clustering Algorithm for Binarizing Scene Text Images  

Kim Ji-Soo (전남대학교 전산학과)
Kim Soo-Hyung (전남대학교 전산컴퓨터정보통신공학부)
Publication Information
The KIPS Transactions:PartB / v.12B, no.7, 2005 , pp. 737-744 More about this Journal
Abstract
In this paper, we propose a three-level color clustering algerian for the binarization of text regions extracted from natural scene images. The proposed algorithm consists of three phases of color segmentation. First, the ordinary images in which the texts are well separated from the background, are binarized. Then, in the second phase, the input image is passed through a high pass filter to deal with those affected by natural or artificial light. Finally, the image Is passed through a low pass filter to deal with the texture in texts and/or background. We have shown that the proposed algorithm is more effective used gray-information binarization algorithm. To evaluate the effectiveness of the proposed algorithm we use a commercial OCR software ARMI 6.0 to observe the recognition accuracies on the binarized images. The experimental results on word and character recognition show that the proposed approach is more accurate than conventional methods by over $35\%$.
Keywords
Scene Text Recognition; Adaptive Binarization; Color Clustering;
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Times Cited By KSCI : 1  (Citation Analysis)
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