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http://dx.doi.org/10.3745/KIPSTB.2009.16-B.3.239

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

Lim, Jun-Sik (전남대학교 전자컴퓨터공학부)
Kim, Soo-Hyung (전남대학교 전자컴퓨터공학부)
Lee, Guee-Sang (전남대학교 전자컴퓨터공학부)
Yang, Hyung-Jung (전남대학교 전자컴퓨터공학부)
Lee, Myung-Eun (전남대학교 전자컴퓨터공학부)
Publication Information
The KIPS Transactions:PartB / v.16B, no.3, 2009 , pp. 239-246 More about this Journal
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.
Keywords
Signboard Recognition; Nonlinear Directional Segment; Feature; Character Recognition;
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