• Annual Conference on Human and Language Technology
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• 1993.10a
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• pp.503-518
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• 1993

봉네트는 온라인 한글 필기 글씨 모델이다 [신92]. 글씨를 자소와 연결획의 결합구조로 보고, 각 자소 및 연결획 모델을 정의한 후, 이들을 제자 원리에 따라 네트워크 구조로 설계한 모델이다. 본 논문에서는 봉네트가 소개된 후 지난 일년 동안 수행되었던 실험 및 모델 검증의 결과와 앞으로도 계속될 개선책을 소개하고, 동 모델의 바탕이 된 통계적 인식 이론을 정립하고자 한다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.3
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• pp.132-140
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• 1994

SCRIPT (Symbol/Character Recognition In Pen-based Technology) is an algorithm for on-line recognition of handwriting Hangeul. English upperacase letters, decimal digits, and some keyboard symbols. The shape of handwriting symbols has a large variation even when written by the same person. Though the feature analysis approach using a conventional decision tree is efficient, it is not robust under shape variations and prone to misclassification. Thus, a new method to overcome this shortcoming is necessary. In this paper, a feature analysis algorithm using two fuzzy decision trees which utilize the hierarchical property of the pattern is proposed. The first tree is used to represent the stroke shape, and the other tree is used to represent the relation between the strokes. since this method stores various possibilities. it is robust to shape variations and can readily modify false selections. In addition, there is a large increase in the recognition rate of high-level patterns due to low-level candidated. Experimental results show 91% recognition rate for Hangeul at the recognition speed of 0.33 second per character, and the recognition rate of alphanumerics and some keyboard symbols is 95% at 0.08 second per symbol. This is 8~18% increase in the recognition rate over th method not applying fuzzy decision trees.

• Journal of the Institute of Convergence Signal Processing
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• v.1 no.1
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• pp.64-73
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• 2000

The Korean character recognition technology for user interface in multimedia terminal requires fast processing time and high recognition rate. In this paper, we propose an phoneme and character recognition technology which uses characteristic information of korean and features of input strokes, i.e, feature point, feature vector, virtual vector, position relation between strokes. And, a recognition both phoneme and character by the various writing types of users uses korean database. The Korean database has been constructed by the characteristic information of korean and phoneme models which have various stroke information. Also, we use successive processing by the position relation between strokes and backtracking processing by the modification processing of stroke numbers which composed of each phoneme. This method reduces the complex processing of phoneme separation. The proposed on-line korean character recognition system has obtained 13msec average character processing time and correct recognition rate more than $95{\%}$ In a recognition experiment, where we tested 600 characters written by 10 people among 1,200 words.

• Journal of the Korea Computer Industry Society
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• v.3 no.5
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• pp.575-584
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• 2002

On-line handwritten character recognition be achieved successful results since effectively neural networks divided the letter which is the time ordering of strokes and stroke position. But off-line handwritten character recognition is in difficulty of incomplete preprocessing because has not information of motion or time and has frequently overlap of the letter and many noise occurrence. consequently off-line handwritten character recognition needs study of various methods. This paper apply watershed algorithm to preprocessing for off-line handwritten hangul character recognition. This paper presents effective method in four steps in watershed algorithm as consider execution time of watershed algorithm and quality of result image. As apply watershed algorithm with effective structure to preprocessing, can get to the good result of image enhancement and binarization. In this experiment, this paper is estimate the previous method with this paper method for execution time and quality in image. Average execution time on the previous method is 2.16 second and Average execution time on this paper method is 1.72 second. While this paper method is remove noise effectively with overlap stroke, the previous method does not seem to be remove noise effectively with overlap stroke.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.769-776
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• 1987

A method of on-line recognition of Korean characters (Hangeul) by augmented conterxt free grammar is described in this paper. Syntactic analysis with context free grammar oftern has ambiguity. Insufficient description of relations among Hangrul sub-patterns causes this ambiguity can be determined through repetition of experiments. Flexible syntactic analysis is executed by adapting the condition to the (advice)part of augmented context free grammar. The ratio of correct recognition of this method is more than 99%.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.9
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• pp.1231-1238
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• 1995

In on-line character recognition, to segment input character is important. This paper proposes an internal character segmentation algorithm. The internal segmentation algorithm produces candidate words by considering possible combinations of Korean alphabets. In this process, we make use of projections of strokes onto the horizontal axis to remove ambiguities among candidate words. As a result of experiments, the internal segmentation algorithm shows better performance than external segmentation algorithm as the gap between sample characters becomes smaller.

• Annual Conference of KIPS
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• 2000.10b
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• pp.1653-1656
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• 2000

필기체 서체 인식은 온라인 문자 인식 시스템에서 주로 사용되는 시스템이다. 또한 오프라인 문자 인식 시스템은 문자 인식에만 초점이 맞추어져 있는 상황이다. 본 논문에서는 오프라인 방식으로 기초의 문자 인식에만 머물던 시스템을 문자 익히기까지 행할 수 있는 시스템으로 확장하는 방법을 제안 하고자 한다. 이를 위해 신명조체 80포인트에 대한 표준문자패턴을 생성하고, 유사도함수를 정의하며 이를 통해 입력 문자 패턴과의 유사성을 계산하여 문자 익히기를 행하고자 한다.

• Journal of KIISE:Software and Applications
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• v.29 no.10
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• pp.684-693
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• 2002

Electronic Ink is a stored data in the form of the handwritten text or the script without converting it into ASCII by handwritten recognition on the pen-based computers and Personal Digital Assistants(PDAs) for supporting natural and convenient data input. One of the most Important issue is to search the electronic ink in order to use it. We proposed and implemented a script matching algorithm for the electronic ink. Proposed matching algorithm separated the input stroke into a set of primitive stroke using the curvature of the stroke curve. After determining the type of separated strokes, it produced a stroke feature vector. And then it calculated the distance between the stroke feature vector of input strokes and one of strokes in the database using the dynamic programming technique. We did various experiments and our algorithm showed high matching rate over 97.7% for only the Korean script and 94% for the data mixed Korean with the Chinese character.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.1
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• pp.29-37
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• 1989

This paper presents an application of the extended DP matching method to the on-line recognition of cursive Hangeul (Korean characters). We decrease the number of matching's objects by performing rough classification matching which makes the best use of features in the first and the last segment of Hangeul. By adding the extraction function of the basic character patterns to DP matching method, we try to calculate precisely the difference among Hangeul. The extraction of the basic character patterns is done by examining the features of segments in character. Applying the extended DP matching method to the on-line recognition of cursive Hangeul, absorption of writing motion and stable separation of strokes can be performed with flexibility.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.32B no.1
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• pp.147-153
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• 1995

In this paper, we propose the position- and size-independent handwritten on-line Korean character recognition system using multilayer neural networks which are trained with error back-propagation learning algorithm and the features of Hanguel consonants and vowels. Starting point, end point, and three vectors from starting point to end point of each stroke of characters inputted from mouse or tablet are applied as inputs of neural networks. If double consonants and vowels are separated by single consonants and vowels, all consonants and vowels have at most four strokes. Therefore, four neural networks learn the consonants and the vowels having each number of strokes. Also, we propose the algorithm of separating the consonants and vowels and constructing a character.