Three-Level Color Clustering Algorithm for Binarizing Scene Text Images


The KIPS Transactions:PartB , Vol. 12, No. 7, pp. 737-744, Dec. 2005
10.3745/KIPSTB.2005.12.7.737,   PDF Download:

Abstract

In this paper, we propose a three-level color clustering algorithm 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%.


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Cite this article
[IEEE Style]
J. S. Kim and S. H. Kim, "Three-Level Color Clustering Algorithm for Binarizing Scene Text Images," The KIPS Transactions:PartB , vol. 12, no. 7, pp. 737-744, 2005. DOI: 10.3745/KIPSTB.2005.12.7.737.

[ACM Style]
Ji Soo Kim and Soo Hyung Kim. 2005. Three-Level Color Clustering Algorithm for Binarizing Scene Text Images. The KIPS Transactions:PartB , 12, 7, (2005), 737-744. DOI: 10.3745/KIPSTB.2005.12.7.737.