• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.11
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• pp.140-149
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• 1996

Cursive characters are written on an economical principle to reduce the motion of a pen in the limit of distinction between characters. That is, the pen is not lifted up to move for writing a next stroke, the pen is not moved at all, or connected two strokes chance their shapes to a similar and simple shape which is easy to be written. For these reasons, strokes and korean alphabets are not only easy to be changed, but also difficult to be splitted. In this paper, we propose a curvature stroke modeling method for splitting and matching by using a structural primitive. A curvature stroke is defined as a substroke which does not change its curvanture. Input strokes handwritten in a cursive style are splitted into a sequence of curvature strokes by segmenting the points which change the direction of rotation, which occur a sudden change of direction, and which occur an excessive rotation Each reference of korean alphabets is handwritten in a printed style and is saved as a sequence of curvature strikes which is generated by splitting process. And merging process is used to generate various sequences of curvature strikes for matching. Here, it is also considered that imaginary strokes can be written or omitted. By using a curvature stroke as a unit of recognition, redundant splitting points in input characters are effectively reduced and exact matching is possible by generating a reference curvature stroke, which consists of the parts of adjacent two korean alphasbets, even when the connecting points between korean alphabets are not splitted. The results showed 83.6% as recognition rate of the first candidate and 0.99sec./character (CPU clock:66MHz) as processing time.

• Korean Journal of Cognitive Science
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• v.3 no.1
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• pp.61-78
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• 1991

This paper descibes the study of application of a modified Neocognitron model with backward path for the recognition of Hangul(Korean) syllabic characters. In this original report, Fukushima demonstrated that Neocognitron can recognize hand written numerical characters of $19{\times}19$ size. This version accepts $61{\times}61$ images of handwritten Hangul syllabic characters or a part thereof with a mouse or with a scanner. It consists of an input layer and 3 pairs of Uc layers. The last Uc layer of this version, recognition layer, consists of 24 planes of $5{\times}5$ cells which tell us the identity of a grapheme receiving attention at one time and its relative position in the input layer respectively. It has been trained 10 simple vowel graphemes and 14 simple consonant graphemes and their spatial features. Some patterns which are not easily trained have been trained more extrensively. The trained nerwork which can classify indivisual graphemes with possible deformation, noise, size variance, transformation or retation wre then used to recongnize Korean syllabic characters using its selective attention mechanism for image segmentation task within a syllabic characters. On initial sample tests on input characters our model could recognize correctly up to 79%of the various test patterns of handwritten Korean syllabic charactes. The results of this study indeed show Neocognitron as a powerful model to reconginze deformed handwritten charavters with big size characters set via segmenting its input images as recognizable parts. The same approach may be applied to the recogition of chinese characters, which are much complex both in its structures and its graphemes. But processing time appears to be the bottleneck before it can be implemented. Special hardware such as neural chip appear to be an essestial prerquisite for the practical use of the model. Further work is required before enabling the model to recognize Korean syllabic characters consisting of complex vowels and complex consonants. Correct recognition of the neighboring area between two simple graphemes would become more critical for this task.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.10
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• pp.1991-2000
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• 1994

In this paper, an alagorithm with six types of classification is suggested for the recognition system of hand-written Korean characters. After thinning process and truncating process for noise redection. The input images are used generalized by $64\times64$ size. The six type classification is composed of preliminary and secondary classification process by using the learning algoritm of multi-layer perceptron. Subblock Hough transform is used as local feature and sampling Hough transform is used as global feature. Experiment is conducted for 1800 characters which is written 31 times per each type by 10 persons. The 90% recognition rate is resulted by the preliminary classification of detection the final consonant and by the secondary classification of detecting the vowels.

• Annual Conference on Human and Language Technology
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• 1989.10a
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• pp.137-141
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• 1989

본 논문에서는 신경망 구조의 한 모델인 feed-forward multi-layered network에 역전파 학습(back-propagation learning) 기법을 이용하여 필기체 한글 숫자를 인식하고 그 가능성을 보였다. 문자 인식에 있어 입력 대상의 모양이 왜곡되거나, 대상의 크기 혹은 위치의 변화 등과 같은 잡음 (noise)에 대해서 정확히 대상을 인식하는 데는 대상의 구조 추출에 크게 관여되므로 한글의 구조 추출에 적합하다고 생각되는 bar mask 투사법을 제안하였다. 모델의 학습을 필기체 한글 숫자 16자의 입력 패턴과 타겟 ( target) 입력의 쌍을 이용해 학습시켰다. 또한, 모델의 인식 정도를 측정해 보기 위해 시험패턴을 적용하여 훈련된 패턴과 훈련되지 않은 패턴간의 인식률을 비교하여 보았다.

• 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%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.3 no.1
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• pp.185-196
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• 1999

In this dissertation, a new off-line recognition system is proposed using a subgraph pattern, neural network. After thinning is applied to input characters, balance having a noise elimination function on location is performed. Then as the first step for recognition procedure, circular elements are extracted and recognized. From the subblock HT, space feature points such as endpoint, flex point, bridge point are extracted and a subgraph pattern is formed observing the relations among them. A region where vowel can exist is allocated and a candidate point of the vowel is extracted. Then, using the subgraph pattern dictionary, a vowel is recognized. A same method is applied to extract horizontal vowels and the vowel is recognized through a simple structural analysis. For verification of recognition subgraph in this paper, experiments are done with the most frequently used Myngjo font, Gothic font for printed characters and handwritten characters. In case of Gothic font, character recognition rate was 98.9%. For Myngjo font characters, the recognition rate was 98.2%. For handwritten characters, the recognition rate was 92.5%. The total recognition rate was 94.8% with mixed handwriting and printing characters for multi-font recognition.

• Journal of the Korean Institute of Intelligent Systems
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• v.6 no.3
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• pp.14-24
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• 1996

A fuzzy classifier which needs various analyses of features using genetic algorithms is proposed. The fuzzy classifier has a simple structure, which contains a classification part based on fuzzy logic theory and a rule generation ation padptu sing genetic algorithms. The rule generation part determines optimal fuzzy membership functions and inclusior~ or exclusion of each feature in fuzzy classification rules. We analyzed recognition rate of a specific object, then added finer features repetitively, if necessary, to the object which has large misclassification rate. And we introduce repetitive analyses method for the minimum size of string and population, and for the improvement of recognition rates. This classifier is applied to three examples of the classification of iris data, the discrimination of thyroid gland cancer cells and the recognition of confusing handwritten and printed numerals. In the recognition of confusing handwritten and printed numerals, each sample numeral is classified into one of the groups which are divided according to the sample structure. The fuzzy classifier proposed in this paper has recognition rates of 98. 67% for iris data, 98.25% for thyroid gland cancer cells and 96.3% for confusing handwritten and printed numeral!;.

• 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.

• The KIPS Transactions:PartB
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• v.14B no.5
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• pp.391-400
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• 2007

An interactive Hangul input method using the numeric phone keypad, which is applicable for mobile devices is introduced. In the proposed method, user only selects the corresponding keys by single tapping, for the alphabet of Korean letter which is desired to enter. The interface generates the subset of eligible letters for the key sequence, then the user selects the desired letter in the set. Such an interactive approach transforms the text entry interface into a multi-level interactive letter-oriented style, from the preexisting passive and single-level alphabet-oriented interface. The annoyance of key-operations, the major disadvantage of the previous methods, derived from multi-tap to clear the ambiguity of multi-assigned alphabets for the Hangul automata, can be eliminated permanently, while the additional letter selection procedure to finalize the desired letter is essential. Also the complexity of Hangul text entry is reduced since all letters can be compounded from basic alphabet selection of the writing sequence order. The advantage and disadvantage of the proposed method are analyzed through comparing with pre-existing method by experiments.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.95-98
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• 2002

문자인식 또는 음성인식을 위해 사용되어지는 CHMM(Continuous Hidden Markov Model)모델은 일반적으로 모델의 상태수를 일정한 수로 고정하는 고정 상태수 모델 구조를 가지고 있으나, 이는 개별적인 인식 단위의 특성을 고려하지 않은 경우로써 이를 고려한 가변 상태수 모델을 사용할 경우 인식률 향상을 기대할 수 있다. 개별적인 인식 단위에 적합한 모델 상태수를 결정하는 방법으로 파라미터 히스토그램 방법과, BIC(Bayesian Information Criterion)방법을 사용하는 것이 대표적이다. 이들 방법들은 개별적인 인식단위의 우도값만을 향상시키기 위한 방법으로 전체인식률과 직접적으로 비례하지는 않는다. 따라서, 본 논문에서는 고정 상태수를 갖는 모델 적용 방법과 인식단위별 상태수 변화에 따른 인식률을 비교하였으며, 이를 바탕으로 각 모델별 상태수를 달리하는 가변 상태수 CHMM모델 구성 방법을 제안한다. 제안된 가변상태수 모델의 유효성을 확인하기 위해 음성/문자 공용인식기 중 필기체 문자 인식에 적용한 결과 제안한 LM(Local Maximum)으로 구성된 가변 상태수 모델이 MLE와 BIC로 구성된 모델과 인식률 면에서는 거의 동일한 성능을 유지하면서 전체 상태수는 MLE 모델에 비해 $31\%$, BIC로 구성된 모델에 비해 $22\%$ 감소를 나타내어 제안한 모델의 유효성을 확인할 수 있었다.