• The Journal of the Korea Contents Association
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• v.10 no.4
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• pp.438-446
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• 2010

For language information processing that includes both Hangul and Hanja, an electronic dictionary supporting Hangul and Hanja simultaneously is necessary. This paper examined statistical information on Hanja entries of Korean Unabridged Dictionary such as the number of entries that include Hanja based on the KSC-5601 character set, the frequency of the pronunciation and meaning of each character of Hanja included in the entries, the frequency per part of speech of Hanja in entries and the average number of Hanja characters per entry. At least one or more of Hanja characters appear in 303,951 entries out of 440,594, accounting for 68.99% of the total. 858,595 characters of Hanja are included in the 440,594 entries, which is 1.95 Hanja characters per entry. As the average syllable length of the entries is 3.56 and the average count of the Hanja characters per entry is 1.96, it can be said that 54.7% of all the characters of the entries are in Hanja. Among 4,888 Hanja character codes, 4,660 are used once or more, whereas 228 Hanja codes never appear in any entry. There were 5 characters which appear more than 4,000 times. A total of 858,595 Hanja characters used in all the entries correspond to 471 Hangeul codes.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.499-508
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• 2022

A lot of research has been carried out on the Hangeul generation model using deep learning, and recently, research is being carried out how to minimize the number of characters input to generate one set of Hangul (Few-Shot Learning). In this paper, we propose a CKFont2 model using only 14 letters by analyzing and improving the CKFont (hereafter CKFont1) model using 28 letters. The CKFont2 model improves the performance of the CKFont1 model as a model that generates all Hangul using only 14 characters including 24 components (14 consonants and 10 vowels), where the CKFont1 model generates all Hangul by extracting 51 Hangul components from 28 characters. It uses the minimum number of characters for currently known models. From the basic consonants/vowels of Hangul, 27 components such as 5 double consonants, 11/11 compound consonants/vowels respectively are learned by deep learning and generated, and the generated 27 components are combined with 24 basic consonants/vowels. All Hangul characters are automatically generated from the combined 51 components. The superiority of the performance was verified by comparative analysis with results of the zi2zi, CKFont1, and MX-Font model. It is an efficient and effective model that has a simple structure and saves time and resources, and can be extended to Chinese, Thai, and Japanese.