Recognition of Korean Text in Outdoor Signboard Images Using Directional Feature and Fisher Measure


KIPS Transactions on Software and Data Engineering, Vol. 16, No. 3, pp. 239-246, Mar. 2009
10.3745/KIPSTB.2009.16.3.239, Full Text:

Abstract

In this paper, we propose a Korean character recognition method from outboard signboard images. We have chosen 808 classes of Korean characters by an analysis of frequencies of appearance in a dictionary of signboard names. The proposed method mainly consists of three steps: feature extraction, rough classification, and coarse classification. The first step is to extract a nonlinear directional segments feature, which is immune to the distortion of character shapes. The second step computes an ordered set of 10 recognition candidates using a minimum distance classifier. The last step reorders the recognition candidates using a Fisher discriminant measure. As experimental results, the recognition accuracy is 80.45% for the first choice, and 93.51% for the top five choices.


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Cite this article
[IEEE Style]
J. S. Lim, S. H. Kim, G. S. Lee, H. J. Yang and M. E. Lee, "Recognition of Korean Text in Outdoor Signboard Images Using Directional Feature and Fisher Measure," KIPS Journal B (2001 ~ 2012) , vol. 16, no. 3, pp. 239-246, 2009. DOI: 10.3745/KIPSTB.2009.16.3.239.

[ACM Style]
Jun Sik Lim, Soo Hyung Kim, Guee Sang Lee, Hyung Jung Yang, and Myung Eun Lee. 2009. Recognition of Korean Text in Outdoor Signboard Images Using Directional Feature and Fisher Measure. KIPS Journal B (2001 ~ 2012) , 16, 3, (2009), 239-246. DOI: 10.3745/KIPSTB.2009.16.3.239.