• Proceedings of the IEEK Conference
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• 2002.07b
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• pp.916-919
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• 2002

This paper introduces the problem of typeface classification of Hangul characters and proposes features for typeface classification among Serif and Sans-serif classes. Serif classes have a small decorative stroke around the beginning of vertical strokes, while Sans-serif classes have no serif. Therefore, the serif part is first segmented from the vertical strokes, and the direction of the serif is computed as the feature for Hangul typeface identification. To evaluate the performance of the proposed system, we used 3,000 characters extracted from Korean documents - 1,500 from Serif fonts, other 1,500 from Sans-serif fonts.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.76-80
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• 1993

In this paper we discuss optimization of neural network parameters, such as inclination of the sigmoid function, the numbers of the input layer's units and the hidden layer's units, considering application to recognition of hand written Hangul. Hangul characters are composed of vowels and consonants, and basically classified to six patterns by their positions. Using these characteristics of Hangul, the pattern of a given character is determined by its peripheral distribution and the other features. After then, the vowels and the consonants are recognized by the optimized neural network. The constructed recognition system including a neural network is applied to non-learning Hangul written by some Korean people, which are the names randomly taken from Korean spiritual and cultural research institute.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.5
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• pp.43-51
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• 1993

A new Hangul code called Truecode is developed for accomodating to the future computing environments of graphical user interface and multimedia as well as for corresponding with the invention principle of Hangul. Truecode is not a forced two-byte code of syllable unit, as completion-type of combination-type, currently used, but a one byte code of phoneme unit, which can represent initial consonant, vowel, and final consonant each. It is quite different from three-byte code of syllable unit and also does not require the fill code used for three-byte code. We expect great contribution to the Hangul culture from Truecode's some important following features. It can express all the Korean characters we may imagine and does not cause any problem in communication. As well as we may use direct connection font, we can assign ont-to-one correspondence between Truecode and a keyboard with three sets. Truecode has a good advantage in developing application softwares of Hangul and it can nicely be applied to the fields of speech recognition and artificial intelligence using natural language.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.27-37
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• 1991

A recognition algorithm for Hangul is developed by structural analysis to Hangul in this theses. Four major procedures are proposed : preprocessing, type classification, separation of consonant and vowel, recognition. In the preprocessing procedure, the thinning algorithm proposed by CHEN & HSU is applied. In the type classification procedure, thinned Hangul image is classified into one of six formal types. In the separation of consonant and vowel procedure, starting from branch-points which are existed in a vowel, character elements are separated by means of tracing branch-point pixel by pixel and comparison with proposed templates. In the same time, the vowels are recognized. In the recognition procedure, consonants are extracted from the separated Hangul character and recognized by modified Crossing method. Recognized characters are converted into KS-5601-1989 codes. The experiments show that correct recognition rate is about 80%-90% and recognition speed is about 2-3 character persecond in three types of different input data on computer with 80386 microprocessor.

• Journal of the Korean Institute of Oriental Medical Informatics
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• v.11 no.2
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• pp.65-82
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• 2005

The findings from this study are summarized as follows: 1. Hangul 2004 has 16,023 Chinese Character codes. Among them, 15,231 Chinese Character codes are searched by DB, the others are unsearchable codes. 2. Among 15,231 Chinese Character codes of Hangul 2004, 2,471 Chinese Character codes are converted into 2,232 Simplified Chinese Character codes by Traditional and Simplified Chinese Character Converting program in Hangul 2004. 3. The 5th edition TCM-MeSH has 6,385 thesauruses and 2,142 kinds of Chinese Characters. 4. If we use Simplified Chinese Character of Hangul 2004 to search for TCM-MeSH, we will find 94.3% of TCM-MeSH. But If we use Traditional Chinese Character of Hangul 2004 to search for TCM-MeSH, we will find only 34.2% of TCM-MeSH.

• Cross-Cultural Studies
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• v.30
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• pp.223-244
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• 2013

Sino-Korean words make almost 70% of Korean words. Chinese Characters are very different from Hangul (Korean alphabet system) in form and they are semantic symbols. Therefore Korean and foreign students are very likely to have difficulty in mastering the Sino-Korean characters. This paper aims at reviewing the problems of teaching Chinese characters to Koreans and foreigners in Korea, and proposing how to teach them effectively. For this purpose, we first look into the realities of the national system of Chinese characters' education, and then suggest more effective instructions in teaching Chinese characters.

• MALSORI
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• no.19_20
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• pp.29-32
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• 1990

The Arae Hangul wordprocessor is one of the most famous software in Korea. It is very useful to linguists In that it has various systems of foreign characters and enables us to edit enough new ones freely. It has already a broad system of the IPA, which cannot be used for narrow transcription. The writer proposes here a full system of the IPA revised to 1979, which you can get from the Phonetic Society of Korea or Hangul & Computer Co., Ltd., free of charge. To use the new system of the IPA in the Arae Hangul wordprocessor, you must replace six revised files: (1) SPECIAL2.SFT, (2) SPECIAL2.PFT, (3)SPECIAL2.PFT, (4)ASCII.SFT, (5)ASCII.PFT, (6)ASCII.LFT.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.12
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• pp.1956-1964
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• 1989

In this paper, a new method of Hangul recognition is proposed to solve the problems of misrecognition owing to the contacts of FCEs (Fundamental Character Elements) in a Hangul pattern. Structures of FCFs are represented with PAG(Programmed Array Grammar) to recognize an input pattern on 2-D. array of pels., and the unnecessary deformation of the conventional approach can be eliminated by using PEACE parsing which extracts primitives and computes attributes in the course of analyzing the structure of an input pattern. Also, primitive transformation at contacts can afford to confirm all the possible structures of an input pattern and solve the problem of misrecognition owing to the contacts of FCEs. The recognition rate of proposed method for printed Hangul characters shows 96.2% for 1978 Gothic-letters and 92.0% for 1920 Myng-style-letters, respectively.

• Korean Journal of Cognitive Science
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• v.2 no.1
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• pp.33-50
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• 1990

Recently, neural networks have been proposed as computaional models for hard prlblems that the brain appears to solve easily. This paper proposes a hierarchical network which practically recognizes printed Hangul characters based on the various psychological stueies. This system is composed of a type classification netwotk and six recognition networks. The former clessifier input character images into one of the six thper by their overall sturcture, and the latter further classify them into character code. Extperiments with most frequently used 990 printed hangul characters conform the superiority of the propsed system. After all, neural nework approach turns out to be very reasonable through a comparison with statistical classifier and an analysis of mis-classification and generalization capability.

• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1273-1280
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• 1996

In order perform high accuracy recognition of text recognition systems, the recognized text must be processed through a post-processing stage using contextual information. We present a system that combines multiple knowledge sources to post-process the output of an optical character recognition(OCR) system. The multiple knowledge sources include characteristics of word, wrongly recognized types of Hangul characters, and Hangul word learning In this paper, the wrongly recognized characters which are made by OCR systems are collected and analyzed. We imput a Korean dictionary with approximately 15 0,000 words, and Korean language texts of Korean elementary/middle/high school. We found that only 10.7% words in Korean language texts of Korean elementary/middle /high school were used in a Korean dictionary. And we classified error types of Korean character recognition with OCR systems. For Hangul word learning, we utilized indexes of texts. With these multiple knowledge sources, we could predict a proper word in large candidate words.