• Journal of Science Education
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• v.33 no.2
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• pp.346-352
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• 2009

The purpose of this research was to analyze the chinese chemistry terminology in chemistry unit of 7th grade science textbooks in 7th curriculum and find relationship between student understanding and difficulty index for chinese preferred students and non-chinese preferred students. The chinese terminology in 7th curriculum was reduced less than that of 6th curriculum but still was over 70%. Students had difficulties in understanding of abstract terminologies and science terms but thought easily the concrete, common terminologies. The tendency of student understanding was similar to that of difficulty index. For chinese chemistry terminology, understanding of chinese preferred students was higher than that of non-chinese preferred students. For easily translated chemistry terminology, there was no significant difference but both were showed the improved understanding. Therefore student understanding should be improved in science if science textbooks would be written by easily translated chemistry terminology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.70-76
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• 2010

In this paper, we propose a personalized Chinese character education system using RankBoost algorithm on a smartphone. In a typical Chinese character education scenario, a trainee is supplied with a finite number of Chinese characters as an input set in the beginning. And, as the training session repeats, the trainee will notice her/his difficult characters in the set which she/he hardly answers. Those characters reflect their personalized degrees of difficulty. Our proposed system constructs these personalized degrees of difficulty using RankBoost algorithm. In the beginning, the algorithm start with the set of Chinese characters, of which each is associated with the same weight values. As the training sessions are repeated, the algorithm increase the weights of Chinese characters that the trainee mistakes, thereby eventually constructs the personalized difficulty degrees of Chinese characters. The proposed algorithm maximizes the educational effects by having the trainee exposed to difficult characters more than easy ones.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.11
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• pp.1175-1184
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• 1992

A new classification method and recognition algorithms for printed Korean and Chinese character is studied for Korean text which contains both Korean and Chinese characters. The proposed method utilizes structural features of the vertical and horizontal vowel in Korean character. Korean characters are classified into 6 groups. Vowel and consonant are separated by means of different vowel extraction methods applied to each group. Time consuming thinning process is excluded. A modified crossing distance feature is measured to recognize extracted consonant. For Chinese character, an average of stroke crossing number is calculated on every characters, which allows the characters to be classified into several groups. A recognition process is then followed in terms of the stroke crossing number and the black dot rate of character. Classification between Korean and Chinese character was at the rate of 90.5%, and classification rate of Ming-style 2512 Korean characters was 90.0%. The recognition algorithm was applied on 1278 characters. The recognition rate was 92.2%. The densest class after classification of 4585 Chinese characters was found to contain only 124 characters, only 1/40 of total numbers. The recognition rate was 89.2%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.16 no.3
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• pp.232-247
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• 1991

This paper proposes a new classification algorithm using characteristic and structural information of printed Hanja as preliminary stages of Hanja-character recognition. Hanja is difficult for not only recognition but classification as many character and complicated structure. In this paper, to solve thie problem, extracted common subpattern in classified pattern after processing type classification fot Hanja pattern. First, we extracted subpattern, after we process preprecessing about input of character pattern, extracting directional segment, labeling on 4-directional pattern and 12 type classified using structural information based on the subpattern existing region of character pattern. Though the experiment, this study obtained that classified rate of Hanja is 93.07% on 1800 character of educational Hanja and 90.12% on 4888 character of KS C5601 standard TRIGEM LBP Hanja font and saw that as extracting subpattern at classified data was this paper possibly applied to the recognition.

• 중국학논총
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• no.65
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• pp.167-192
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• 2020

城市化, 一方面具有特殊性, 如同中國; 另一坊面具有普遍性, 如同地球上的大部分社會已經都經歷過或者正在經歷著 。所謂的全球規模的城市化就是從圍繞城市化的中國之特殊性及全球規模的普遍性中, 或者是從中國的城市化所呈現出來的具體性中抽象出來的, 這說明城市化, 中國, 中國的城市化之間具有密不可分的關系。由此, 本文試圖運用嶄新的方法論-"城市文化研究"來探討這種相互關系和漢字的溝通(不)可能性又是具有何等關系, 如何預測由此產生的前景等問題。爲此, 本文第一步基於"行星城市化(planetary urbanization: urbanisation planétaire)"的理論性, 實踐性背景闡明了全球規模的城市化和中國的關系。第二步在貫穿整個農村社會和城市社會的"城市革命"框架下對中國的城市化進行了細緻分析。第三步首先論證在國家不斷建造城市空間的背景下由於國家及政黨深入幹預中國的現實空間, 虛擬空間, 及與此具有緊密關系的精神空間, 因此不可能建立現實空間和虛擬空間之間辯證關系, 然後進而診斷漢字的溝通(不)可能性, 最後通過分析"城市革命"的另一個側面來提出其替代方案。

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.526-534
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• 2006

이 논문에서는 트루타입 폰트의 글자 외곽선 데이터를 이용하여 자동으로 한자의 획을 분리하고 획 순서를 정하는 방법을 제안한다. 트루타입 폰트에는 글자의 외곽선 정보가 벡터 형식으로 저장되어 있으며, 이러한 벡터들은 일정한 규칙으로 배열되어 있다. 이와 같은 벡터들의 배치를 이용하여 한자의 획이 될 수 있는 벡터들의 집합을 조합하여 독립적인 획을 분리해 내고, 글자를 획 별로 분리하여 본래 트루타입 폰트의 저장 형식과 동일한 파일 형식으로 저장한다. 또한 분리된 모든 획에 대하여, 획 이름을 정의하고, 정의된 획들 간의 위치와 상관관계를 이용하여, 획 사이의 우선순위를 결정하여 획 순서를 부여한다. 이 작업들은 사람의 작업 없이 순수하게 자동으로 이루어지므로, 시간과 노력을 최소화 할 수 있다. 게다가, 획 별로 분리되고 순서대로 정리된 한자들은 트루타입 폰트에 저장되어 있는 모양과 특성을 그대로 가지고 있으므로, 단순히 폰트 자체로써 사용할 수도 있을 뿐만 아니라, 한자 학습 컨텐츠로도 이용이 가능하며, 각종 애니메이션 효과 등 다양한 분야에서 융통성 있게 활용될 수 있다.

• Annual Conference on Human and Language Technology
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• 1995.10a
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• pp.244-252
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• 1995

본 연구는 한자를 이용하여 시각 정보 (Visual Information)와 음성 정보(Phonological Information)가 단어 재인과 단어 명명 과정에 어떻게 영향을 주는 지를 조사하기 위하여 실시되었다. 기존의 영어를 이용한 연구에서는 시각 정보와 음성 정보를 독립적으로 조작할 수 없었기에 두 요소가 단어 재인에 어떤 영향을 주는 지를 살피는데 어려움이 있었다. 그러나 한자단어를 이용하면 시각 정보와 음성 정보를 독립적으로 조작할 수 있기 때문에 영어 단어를 사용하는 것보다 유리하다. 본 실험에서는 한자 단어를 이용하여 점화 단어 (Prime Word)와 목표 단어(Target Word)간의 시간간격(SOA)을 100 ms, 200 ms, 750 ms, 그리고 2000 ms로 변화시키면서 시간이 흐름에 따라 시각적 유사성과 음성적 유사성에 의한 점화 효과(Priming Effect)가 어떻게 변화하는 지를 조사하였다. 이 실험 결과에 의하면, 100 ms 조건에서는 시각적 유사성에 의한 점화 효과만 있었다. 그러나, 200 ms, 750 ms, 2000 ms 조건들에서는 시각적 유사성뿐만 아니라 음성적 유사성에 의해서도 점화효과가 있었다. 이와 같은 실험 결과는 최초의 한자 단어의 어휘 접근 (Lexical Access)이 시각 정보에 의해 결정됨을 보여주고 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.402-407
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• 2006

본 논문에서는 보다 실용적인 온라인 한자인식기 개발을 위하여 한자 검정 능력 1급 쓰기 수준을 모두 포함하는 한자 필기 데이터로부터 16방향의 체인코드열과 부분획의 구조를 반영하는 구조코드열을 만들어 성능평가를 하였다. 인식 방법으로는 DP 매칭 방법과 HMM을 사용하여 2,362 종류의 한자에 대해 인식 실험을 하였다. 그 결과 체인코드열을 사용한 DP 매칭 방법에 의한 결과가 96.54%로 가장 높은 인식률을 보였으며, 구조코드열을 사용하여 HMM에 의한 인식실험 결과가 95.65%로 그 뒤를 이었다. 인식 속도면에서는 체인코드 보다 코드열의 길이가 짧은 구조코드열을 사용한 방법이 상대적으로 유리했고, 클래스 당 1개의 모델을 사용한 HMM에 의한 방법이 클래스 당 복수개의 모델을 사용한 DP 매칭 방법에 비해 모델의 개수가 훨씬 적기 때문에 속도 면에서 월등히 유리해 더 효율적인 인식 성능을 보인다는 결론을 내릴 수 있었다.

• The Journal of Engineering Research
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• v.1 no.1
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• pp.59-64
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• 1997

More than 2,000 different chinese characters are used daily in Korea newspapers and publications. The large repertoire of character pattern are the main difficulties when machine recognition of chinese characters is concerned. The goal of this paper is to conceive, evaluate and refine techniques for high performance Chinese character recognition. A new character classifier was being developed using prototype creation method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.6
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• pp.802-814
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• 1993

This paper propose an algorithm for preprocessing of character recognition, which classify characters into Hangeul and Hanja. In this study, we use the 9 structural chacteristics of Hanja which isn't affected by deformation of size and style of characters and rates based on character size to classify characters. Firstly, we process the blocking to segment each characters. Secondly, on this segmented characters, we apply algorithm proposed in this paper to classify Hangeul and Hanja. Finally, we classify characters into Hangeul and Hanja, respectively. An experiment with 2350 Hangeul and 4888 Hanja printed Gothic and Mincho style of KS-C 5601 are carried out. We experiment on typeface sample book, newspapers, academic society's papers, magazines, textbooks and documents written out word processor to obtain the classifying rates of 98.8%, 92%, 96%, 98% and 98%, respectively.