• Korean Journal of Cognitive Science
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• v.3 no.2
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• pp.271-289
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• 1992

In this paper,a cursive on-line Hangul recognition system which permits cursive writing between graphemes is presented.In general,the recognition of cursive Hangul writing has a difficulty of graheme segmentation and a complexity in matching procedure due to the increasing number of character candidates.To manage efficiently these problems,we propose a double segmentation method.Based on this segmentation,a recognition algorithm based on the line segment matching is proposed.Through the experimental result,it is show that the proposed recognition method is suitable for the cursive Hangul writings.

• 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 Korea Academia-Industrial cooperation Society
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• v.7 no.5
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• pp.887-894
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• 2006

This paper proposes a new method that detects the intersection points of handwritten Hangul strokes using run-length. The method firstly finds the strokes' width of handwritten Hangul characters using both horizontal and vertical run-lengths, secondly extracts horizontal and vertical strokes of a character utilizing the strokes' width, and finally detects the intersection points of the strokes exploiting horizontal and vertical strokes. The analysis of both the horizontal and the vertical strokes doesn't use the strokes' angles but both the strokes' width and the changes of the run-lengths. The intersection points of the strokes become the candidated parts for phoneme segmentation, which is one of main techniques for off-line handwritten Hangul recognition. The segmented strokes represent the feature for handwritten Hangul recognition.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.1181-1185
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• 1991

Unlike printed character, the recognition of Hand written one has various kinds of difficulties due to the existence of the huge pattern associated with the person who writes. Therefore, in general, recognition of Hand written characters requires an algorithm which takes into consideration of the individual differences. Hangul characters are basically made of straight lines and circles. They can be represented in terms of feature parameters such as the end point of the straight line, the length and the angle. Then all Hangul characters can be represented by the number of basic segments(-, /, vertical bar, O) multiplied by the feature parameters respectively. In this study we propose a method for recognizing Hand written Hangul characters in terms of fuzzy inference.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.6
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• pp.76-85
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• 1993

In this paper, we propose a stroke matching method for the off-line recognition of handprinted Hangul. In this method, the preprocessing steps such as position normalization, contour tracing and thinning are carried out first. Then, after extracting features such as the firection component distribution of contour, the direction component distribution of skeleton, and the distribution of structural feature points, strokes are extracted and matched based on the midpont distribution of the direction and the length of each stroke. In order to reduce the recognition time, a preliminary classification based on the direction component distribution features of the contour is performed. In order to domonstrate the performance of the proposed method, experiments with 520 most frequently used Hangul were performed, and 90.7% of correct recognition rate and 0.46second of recognition time per one character has been obtained. This results reveal that the proposed method can absorb effectively the noise in input character and the variations of stroke slant.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.54-61
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• 2016

Because a word is the basic unit of language processing, studies of the word recognition processing and the variables that contribute to word recognition processing are very important. Word frequency and word length are recognized as important factors on word recognition. This study examined the effects of those two variables on the Korean word recognition processing. In Experiment 1, two types of Hangul words, pure Hangul words and Hangul words with Hanja counterparts, were used to explore the frequency effects. A frequency effect was not observed for Hangul words with Hanja counterparts. In Experiment 2, the word length was manipulated to determine if the word length effect appears in Hangul words. Contrary to the expectation, one syllable words were processed more slowly than two syllable words. The possible explanations for these results and future research directions are discussed.

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.1019-1022
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• 1999

In this paper, we analysis the effect of a dictionary in a handwritten Hangul word recognition problem in terms of its size and the length of the words in it. With our experimental results, we can account for the word recognition rate depending not only on character recognition performance, but also much on the amount of the information that the dictionary contains, as well as the reduction rate of a dictionary.

• KIISE Transactions on Computing Practices
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• v.23 no.5
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• pp.316-321
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• 2017

We analyze the online Hangul handwriting recognition problem (HHR) and present solutions based on recurrent neural networks. The solutions are organized according to the three kinds of sequence labeling problem - sequence classifications, segment classification, and temporal classification, with additional consideration of the structural constitution of Hangul characters. We present a stacked gated recurrent unit (GRU) based model as the natural HHR solution in the sequence classification level. The proposed model shows 86.2% accuracy for recognizing 2350 Hangul characters and 98.2% accuracy for recognizing the six types of Hangul characters. We show that the type recognizing model successfully follows the type change as strokes are sequentially written. These results show the potential for RNN models to learn high-level structural information from sequential data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.3
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• pp.385-392
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• 2006

This paper proposes a new stroke extraction algorithm for phoneme segmentation, which is one of main techniques for off-line handwritten Hangul recognition. The proposed algorithm extracts vertical, slant, and horizontal strokes from phonemes using run-length. The run-length of vertical or slant strokes becomes the width, and also the number of horizontal run-lengths the width. After extracting horizontal strokes from phonemes, the algorithm links two continuous vertical or slant stokes with run-lengths of the strokes' width to represent the features of a character. The extracted strokes can be utilized to recognize a character, using template matching of strokes, which is being adopted in on-line handwritten Hangul recognition.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.1
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• pp.25-30
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• 2000

Hangul is regarded as one of the difficult character set due to the large number of classes and the shape similarity among different characters. Most of the conventional researches attempted to recognize the 2,350 characters which are popularly used, but this approach has a problem or low recognition performance while it provides a generality. On the contrary, recognition of a small character set appearing in specific fields like postal address or bank checks is more practical approach. This paper describes a research for recognizing the handwritten Hangul characters appearing in monetary fields. The modular neural network is adopted for the classification and three kinds of feature are tested. The experiment performed using standard Hangul database PE92 showed the correct recognition rate 91.56%.