• Journal of the Ergonomics Society of Korea
• /
• v.21 no.2
• /
• pp.1-11
• /
• 2002

Classification of Korean characters(alphabets) and frequency data of them are studied that is essential to information process of Korean. We defined a classification of characters using the concept of 'set of 2 parts' and 'set of 3 parts', and we researched frequencies about all combinations of continual two characters. These data would be important basic data to design input device of computer, for example.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.27 no.2
• /
• pp.135-145
• /
• 2023

Recently, as deep learning technology has developed, various deep learning technologies have been introduced in handwritten recognition, greatly contributing to performance improvement. The recognition accuracy of handwritten Hangeul recognition has also improved significantly, but prior research has focused on recognizing 520 Hangul characters or 2,350 Hangul characters using SERI95 data or PE92 data. In the past, most of the expressions were possible with 2,350 Hangul characters, but as globalization progresses and information and communication technology develops, there are many cases where various foreign words need to be expressed in Hangul. In this paper, we propose a model that recognizes and combines the consonants, medial vowels, and final consonants of a Korean syllable using a multi-label classification model, and achieves a high recognition accuracy of 98.38% as a result of learning with the public data of Korean handwritten characters, PE92. In addition, this model learned only 2,350 Hangul characters, but can recognize the characters which is not included in the 2,350 Hangul characters

• Korean Journal of Cognitive Science
• /
• v.3 no.2
• /
• pp.249-270
• /
• 1992

The purpose of this paper is to classify the types of input characters,printed Hangul characters,using Maximum Run Length Projection(MRLP)that is used to extract features of input character.Because the number of Hangul characters is large and its structure is complex,there exists close similarities among characters.This paper,therefore,tried to increment the type classification rate using fuzzy resoning.The Maximum Run Length Projection is very immune to noise,and also useful to extracting the demanding information efficiently.In a test case with the most frequently use 917 printed Hangul characters,it achieved 98.58%correct classification rate.

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

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.1
• /
• pp.49-55
• /
• 2019

The character dialogue created by AI is unnatural when compared with human-made dialogue, and it can not reveal the character's personality properly in spite of remarkable development of AI. The purpose of this paper is to classify characters through the linguistic style and to investigate the relation of the specific linguistic style with the personality. We analyzed the dialogues of 92 characters selected from total 60 movies categorized four movie genres, such as romantic comedy, action, comedy and horror/thriller, using Linguistic Inquiry and Word Count (LIWC), a text analysis software. As a result, we confirmed that there is a unique language style according to genre. Especially, we could find that the emotional tone than analytical thinking are two important features to classify. They were analyzed as very important features for classification as the precision and recall is over 78% for romantic comedy and action. However, the precision and recall were 66% and 50% for comedy and horror/thriller. Their impact on classification was less than romantic comedy and action genre. The characters of romantic comedy deal with the affection between men and women using a very high value of emotional tone than analytical thinking. The characters of action genre who need rational judgment to perform mission have much greater analytical thinking than emotional tone. Additionally, in the case of comedy and horror/thriller, we analyzed that they have many kinds of characters and that characters often change their personalities in the story.

• KIEE International Transaction on Systems and Control
• /
• v.12D no.1
• /
• pp.27-32
• /
• 2002

In this paper, we would study the applicability of neural networks to the recognition process of Korean stenographic character image, applying the classification function, which is the greatest merit of those of neural networks applied to the various parts so far, to the stenographic character recognition, relatively simple classification work. Korean stenographic recognition algorithms, which recognize the characters by using some methods, have a quantitative problem that despite the simplicity of the structure, a lot of basic characters are impossible to classify into a type. They also have qualitative one that It Is not easy to classify characters fur the delicacy of the character farms. Even though this is the result of experiment under the limited environment of the basic characters, this shows the possibility that the stenographic characters can be recolonized effectively by neural network system. In this system, we got 90.86% recognition rate as an average.

• Journal of the Korea Society of Computer and Information
• /
• v.11 no.6 s.44
• /
• pp.99-106
• /
• 2006

Type classification is a very needed step in recognizing huge character set language such as korean characters. Since most previous researches are based on the composition rule of Korean characters, it has been difficult to correctly classify composite vowel characters and problem space was not divided equally for the lack of classification of last consonant which is relatively bigger than other graphemes. In this paper, I Propose a new type classification method in which horizontal vowel is extracted before vortical vowel and last consonants are further classified into one of five small groups based on horizontal projection profile. The new method uses 19 character types which is more stable than previous 6 types or 15 types. Through experiments on 1.000 frequently used character sets and 30.614 characters scanned from several magazines, I showed that the proposed method is more useful classifying Korean characters of huge set.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.354-354
• /
• 2000

In this paper, we would study the applicability of neural networks to the recognition process of Korean stenographic character image, applying the classification function, which is the greatest merit of those of neural networks applied to the various pans so far, to the stenographic character recognition, relatively simple classification work. Korean stenographic recognition algorithms, which recognize the characters by using some methods, have a quantitative problem that despite the simplicity of the structure, a lot of basic characters are impossible to classify into a type. They also have qualitative one that it is not easy to classify characters for the delicacy of the character forms. Even though this is the result of experiment under the limited environment of the basic characters, this shows the possibility that the stenographic characters can be recognized effectively by neural network system. In this system, we got 90.86% recognition rate as an average.

• Journal of the Korean Society of Tobacco Science
• /
• v.4 no.1
• /
• pp.37-42
• /
• 1982

Korean local and introduced varieties were classified by the cluster analysis of correlation and taxonomic distance based on nineteen growth characters. 1. Thirty six varieties can be classified into three groups(I, II, III) by WVGM (weighted variable group method) 2. Major characters for classifying cultivars were days to flowering, number of leaves, leaf length, stem diameter and width of midrib: the five characters seemed to be useful in monothetic classification. 3. Korean varieties were similar to oriental, and japanese varieties to taiwan. 4. WVGM was more accurate and meaningful than classification by WPGM (weighted paired group method) and reticulate diagram of correlation. 5. Characteristics of each group: Group I closely related to many leaves, late of maturity and broad leaf type, Group II related to medium leaves, late of maturity and narrow leaf type, Croup 19 related to few leaves, early of maturity and medium leaf type respectively.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.4
• /
• pp.77-87
• /
• 1994

This paper presents recognition of raised alphanumeric markings on rubber tires for their automatic classification. Raised alphanumeric markings on rubber tires have different characteristics as compared to those of printed characters. In the preprocessing step, we first determine the rotation angle using the Hough transform and align markings, then separate each character using vertical and horizontal projections. In the recognition step, we use several features such as width of a character, cross point, partial projection, and distance feature to recognize characters hierarchically. The computer simulation result shows that the proposed system can be successfully applied to the industrial automation of rubber tires classification.