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http://dx.doi.org/10.15207/JKCS.2019.10.3.015

Hangeul detection method based on histogram and character structure in natural image  

Pyo, Sung-Kook (Dept of Plasma Bio Display, Kwangwoon University)
Park, Young-Soo (Ingenium college of liberal arts, Kwangwoon University)
Lee, Gang Seung (Ingenium college of liberal arts, Kwangwoon University)
Lee, Sang-Hun (Ingenium college of liberal arts, Kwangwoon University)
Publication Information
Journal of the Korea Convergence Society / v.10, no.3, 2019 , pp. 15-22 More about this Journal
Abstract
In this paper, we proposed a Hangeul detection method using structural features of histogram, consonant, and vowel to solve the problem of Hangul which is separated and detected consonant and vowel The proposed method removes background by using DoG (Difference of Gaussian) to remove unnecessary noise in Hangul detection process. In the image with the background removed, we converted it to a binarized image using a cumulative histogram. Then, the horizontal position histogram was used to find the position of the character string, and character combination was performed using the vertical histogram in the found character image. However, words with a consonant vowel such as '가', '라' and '귀' are combined using a structural characteristic of characters because they are difficult to combine into one character. In this experiment, an image composed of alphabets with various backgrounds, an image composed of Korean characters, and an image mixed with alphabets and Hangul were tested. The detection rate of the proposed method is about 2% lower than that of the K-means and MSER character detection method, but it is about 5% higher than that of the character detection method including Hangul.
Keywords
Hangeul detection; Character structural features; Cumulative histogram; DoG;
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Times Cited By KSCI : 3  (Citation Analysis)
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