Text Detection and Binarization using Color Variance and an Improved K-means Color Clustering in Camera-captured Images


The KIPS Transactions:PartB , Vol. 13, No. 3, pp. 205-214, Jun. 2006
10.3745/KIPSTB.2006.13.3.205,   PDF Download:

Abstract

Texts in images have significant and detailed information about the scenes, and if we can automatically detect and recognize those texts in real-time, it can be used in various applications. In this paper, we propose a new text detection method that can find texts from the various camera-captured images and propose a text segmentation method from the detected text regions. The detection method proposes color variance as a detection feature in RGB color space, and the segmentation method suggests an improved K-means color clustering in RGB color space. We have tested the proposed methods using various kinds of document style and natural scene images captured by digital cameras and mobile-phone camera, and we also tested the method with a portion of ICDAR[1] contest images.


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Cite this article
[IEEE Style]
Y. J. Song and Y. W. Choi, "Text Detection and Binarization using Color Variance and an Improved K-means Color Clustering in Camera-captured Images," The KIPS Transactions:PartB , vol. 13, no. 3, pp. 205-214, 2006. DOI: 10.3745/KIPSTB.2006.13.3.205.

[ACM Style]
Young Ja Song and Yeong Woo Choi. 2006. Text Detection and Binarization using Color Variance and an Improved K-means Color Clustering in Camera-captured Images. The KIPS Transactions:PartB , 13, 3, (2006), 205-214. DOI: 10.3745/KIPSTB.2006.13.3.205.