• Title/Summary/Keyword: 한자

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A Study on Characteristics of Actual State of School Mathematics Terms in North Korea (북한의 학교수학 용어의 현상적 특징에 관한 연구)

  • Park Kyo Sik
    • School Mathematics
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    • v.7 no.1
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    • pp.1-15
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    • 2005
  • In this paper, some characteristics of actual state of school mathematics terms in north Korean mathematics textbooks, which were issued in 2000-2002, are discussed. In north Korea, terms are expressed in north Korean orthography. Many Chinese style terms are translated into pure Korean terms, but there are still so many Chinese style terms. It is known that there is deep gap between north and south Korean terms. But actually it is not. This means that integration of north and south Korean terms can be easily realized. It is known that translating Chinese style terms into pure Korean term can be useful in mathematics teaching and learning, but north Korean experiences tell that we should act with prudence in doing so. We need sufficient discussion in case of north Korean terms which are totally different with south Korean terms. Semantical analysis is needed with preference survey.

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A study on the Character Correction of the Wrongly Recognized Sentence Marks, Japanese, English, and Chinese Character in the Off-line printed Character Recognition (오프라인 인쇄체 문장부호, 일본 문자, 영문자, 한자 인식에서의 오인식 문자 교 정에 관한 연구)

  • Lee, Byeong-Hui;Kim, Tae-Gyun
    • The Transactions of the Korea Information Processing Society
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    • v.4 no.1
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    • pp.184-194
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    • 1997
  • In the recent years number of commercial off-line character recognition systems have been appeared in the Korean market. This paper describes a "self -organizing" data structure for representing a large dictionary which can be searched in real time and uses a practical amount of memory, and presents a study on the character correction for off-line printed sentence marks, Japanese, English, and Chinese character recognition. Self-organizing algorithm can be recommenced as particularly appropriate when we have reasons to suspect that the accessing probabilities for individual words will change with time and theme. The wrongly recognized characters generated by OCR systems are collected and analyzed Error types of English characters are reclassified and 0.5% errors are corrected using an English character confusion table with a self-organizing dictionary containing 25,145 English words. And also error types of Chinese characters are classified and 6.1% errors are corrected using a Chinese character confusion table with a self-organizing dictionary carrying 34,593 Chinese words.ese words.

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