• Title/Summary/Keyword: 글립분석

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Intermediate Font Generation based on Shape Analysis of Hangul Glyph (한글 글립의 조형적 분석에 기반한 중간 폰트 생성)

  • Koo, Sang-Ok;Jung, Soon-Ki
    • Journal of KIISE:Computer Systems and Theory
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    • v.36 no.4
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    • pp.311-325
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    • 2009
  • This paper presents a method for analyzing Hangul glyphs with their outline fonts and obtaining intermediate fonts with two different fonts. The glyphs are represented and analyzed hierarchically such as characters, components(letters) and strokes. With the analysis results, we obtain several intermediate glyphs by morphing two different glyphs of same character. For a natural glyph contour morphing, we employ the curve morphing algorithm by weighted mean of strings. In addition, we provide four operations for transformation of glyphs with different topology. As a result, it is illustrated that the proposed Hangul glyphs morphing scheme is useful for new font generation from any exist fonts or handwritings.

Automatic Stroke Extraction of TrueType Font and Handwriting of Hangul (한글 트루타입폰트 및 손글씨의 자동 획 분할 알고리즘)

  • Kwak, Yoon-Seok;Koo, Sang-Ok;Jung, Soon-Ki
    • Proceedings of the Korean Information Science Society Conference
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    • 2008.06b
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    • pp.275-280
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    • 2008
  • 본 논문에서는 한글 글립(glyph)의 형태학적 분석을 통해 자동으로 획을 분할하는 방법을 제안한다. 제안된 방법은 thinning된 한글 글립의 골격(skeleton) 이미지를 기반으로, 획 분리, 획 병합, 그리고 획 볼륨 복원의 세가지 단계를 거쳐 한글의 기본 획들을 추출해 낸다. 실험 결과, 트루타입폰트(TrueType Font)에 대해서는 80%, 손글씨(Handwriting) 글립에 대해서는 72%의 획 분할 정확도를 보였다. 본 논문에서 제안한 방법으로 획득된 획 정보를 이용하여, 향후 한글 손글씨 생성을 위한 연구를 하고자 한다.

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Hangul Component Decomposition in Outline Fonts (한글 외곽선 폰트의 자소 분할)

  • Koo, Sang-Ok;Jung, Soon-Ki
    • Journal of the Korea Computer Graphics Society
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    • v.17 no.4
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    • pp.11-21
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    • 2011
  • This paper proposes a method for decomposing a Hangul glyph of outline fonts into its initial, medial and final components using statistical-structural information. In a font family, the positions of components are statistically consistent and the stroke relationships of a Hangul character reflect its structure. First, we create the component histograms that accumulate the shapes and positions of the same components. Second, we make pixel clusters from character image based on pixel direction probabilities and extract the candidate strokes using position, direction, size of clusters and adjacencies between clusters. Finally, we find the best structural match between candidate strokes and predefined character model by relaxation labeling. The proposed method in this paper can be used for a study on formative characteristics of Hangul font, and for a font classification/retrieval system.