• 중국학논총
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• no.62
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• pp.1-25
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• 2019

作者对韩国教育部指定的"漢文敎育用基礎漢字高等學校用900字"跟中国规范汉字字形, 进行比较分析的结果如下: (1)字形完全一样的(在附录"高中学校用900字"汉字旁边标注为"="), 一共有424个汉字(约占47%); (2)字形相似的(在附录"高中学校用900字"汉字旁边标注为"Δ"), 一共有86个汉字(约占10%); (3)字形不同的(在附录"高中学校用900字"汉字旁边标注为"×"), 一共有389个汉字(约占43%). 字形相似, 不等于字形相同, 所以也应该看作字形不同的字, 属于这两种情况的字合起来, 一共有475个(约占53%). 韩中汉字字形不同的主要来源, 不止"简化字"和"传承字里的新字形", 还有"从一些异体字里选出来的正体字"也和韩国常用汉字字形不同.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.185-196
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• 1999

In this dissertation, a new off-line recognition system is proposed using a subgraph pattern, neural network. After thinning is applied to input characters, balance having a noise elimination function on location is performed. Then as the first step for recognition procedure, circular elements are extracted and recognized. From the subblock HT, space feature points such as endpoint, flex point, bridge point are extracted and a subgraph pattern is formed observing the relations among them. A region where vowel can exist is allocated and a candidate point of the vowel is extracted. Then, using the subgraph pattern dictionary, a vowel is recognized. A same method is applied to extract horizontal vowels and the vowel is recognized through a simple structural analysis. For verification of recognition subgraph in this paper, experiments are done with the most frequently used Myngjo font, Gothic font for printed characters and handwritten characters. In case of Gothic font, character recognition rate was 98.9%. For Myngjo font characters, the recognition rate was 98.2%. For handwritten characters, the recognition rate was 92.5%. The total recognition rate was 94.8% with mixed handwriting and printing characters for multi-font recognition.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.295-302
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• 2010

Example-based super resolution(EBSR) is a method to reconstruct high-resolution images by learning patch-wise correspondence between high-resolution and low-resolution images. It can reconstruct a high-resolution from just a single low-resolution image. However, when it is applied to a text image whose font type and size are different from those of training images, it often produces lots of noise. The primary reason is that, in the patch matching step of the reconstruction process, input patches can be inappropriately matched to the high-resolution patches in the patch dictionary. In this paper, we propose a new patch matching method to overcome this problem. Using an image observation model, it preserves the correlation between the input and the output images. Therefore, it effectively suppresses spurious noise caused by inappropriately matched patches. This does not only improve the quality of the output image but also allows the system to use a huge dictionary containing a variety of font types and sizes, which significantly improves the adaptability to variation in font type and size. In experiments, the proposed method outperformed conventional methods in reconstruction of multi-font and multi-size images. Moreover, it improved recognition performance from 88.58% to 93.54%, which confirms the practical effect of the proposed method on recognition performance.