• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.4
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• pp.451-455
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• 2008

In this paper we embody a system that recognizes the printed alphabet of two font types (Batang, Dodum) inputted by a black-and-white CCD camera and converts it into an editable text form. The image of the inputted printed sentences is binarized, then the rows of each sentence are separated through the vertical projection using the Histogram method, and the height of the characters are normalized to 48 pixels. With the reverse application of the basic principle of the Nonogram puzzle to the individual normalized character, the character is covered with the pixel-based squares, representing the characteristics of the character as the numerical information of the Nonogram puzzle in order to recognize the character through the comparison with the standard pattern information. The test of 2609 characters of font type Batang and 1475 characters of font type Dodum yielded a 100% recognition rate.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.4
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• pp.32-38
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• 2000

It is an important factor to distinguish the kind of the character for increasing recognition rate before the character recognition in the document recognition system composed of the multi-font and multi-letter. All the letters of each country have a various men characteristic in the each composition. In this paper, we used the stroke density as a method to distinguish the letter, and it has been adopted Korean, English and Chinese character. Input data is processed by the normalization to adopt multi-font document. Proposed method has been proved by the results of experiment the fact that the distinction probability of the Korean and English is more than 80%.

• Journal of the Korean Graphic Arts Communication Society
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• v.4 no.1
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• pp.19-26
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• 1986

• Smart Media Journal
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• v.5 no.3
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• pp.30-34
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• 2016

Nowadays, there are a lot of Korean documents, which often need to be identified in one of printed or handwritten text. Early methods for the identification use structural features, which can be simple and easy to apply to text of a specific font, but its performance depends on the font type and characteristics of the text. Recently, the bag-of-words model has been used for the identification, which can be invariant to changes in font size, distortions or modifications to the text. The method based on bag-of-words model includes three steps: word segmentation using connected component grouping, feature extraction, and finally classification using SVM(Support Vector Machine). In this paper, bag-of-words model based method is proposed using SURF(Speeded Up Robust Feature) for the identification of machine printed and handwritten text in Korean documents. The experiment shows that the proposed method outperforms methods based on structural features.

• The Transactions of the Korea Information Processing Society
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• v.4 no.7
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• pp.1873-1880
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• 1997

It is an important factor to distinguish the kind of the character for increasing recognition rate before the character recognition in the document recognition system composed of the multi-font and multi-letters. All the letters of each country have a various unique characteristic in the each composition. In this paper, we used the stroke density as a method to distinguish the letter, and it has been adopted only Korean and English character. Input data is processed by the normalization to adopt multi-font document. Proposed method has been proved by the results of experiment the fact that the distinction probability of the Korean and English is more than 90%.

• The Research Journal of the Costume Culture
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• v.25 no.5
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• pp.611-628
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• 2017

As a plan for establishing Korea's cultural identity and its competitive edge in the world market and for enhancing Korea's cultural status, creative and unique high value-added cultural products need to be developed utilizing our inherent cultural assets. Accordingly, this study focused on the development of the design of fashion cultural products that utilize the convergence of Hangeul our peculiar font style and Korea's cultural heritage, which is registered as part of UNESCO's World Heritage. A design method was devised that converges archetypal images of cultural property with the unique Hangeul font in a way that targets Korea's symbolic architectures. The symbolic architecture includes Korea's world-heritage pagoda architecture, such as Seokgatap pagoda and Dabotap pagoda at Bulguksa temple. It also included the architecture of royal palace, such as Injeongjeon hall at Changdeokgung palace. Finally, it also included the architecture of the fortress wall, such as Paldalmun gate in Hwaseong fortress. Thus, by developing cultural assets made from a convergence between architecture and the Hangeul font as a consumer-product image that has universality, the possibility of cultural products was pursued by applying color planning after an analysis that involved extracting the compositional colors of the flags of the world. This research and approach will lead to opportunities for further progress for Korea's cultural products in the global market as a results of additional recognition for their value, excellence, and universal appeal.

• Journal of Intelligence and Information Systems
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• v.8 no.1
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• pp.27-39
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• 2002

A locating text technique capable of locating and extracting text blocks in various Web images is presented here. Until now this area of work has been ignored by researchers even if this sort of text may be meaningful for internet users. The algorithms associated with the technique work without prior knowledge of the text orientation, size or font. In the work presented in this research, our text extraction algorithm utilizes useful edge detection followed by histogram analysis on the genuine characteristics of letters defined by text clustering region, to properly perform extraction of the text region that does not depend on font styles and sizes. By a number of experiments we have showed impressively acceptable results.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1598-1601
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• 1987

This paper proposes a new set of topological features (primitives) for use with a syntactic recognizer for high-accuracy recognition of printed alphanumeric characters. The recognition is accomplished on nine character groups, where each group has different combinations of four feature points. A skeleton enhancement eliminating isolated points and smoothing irregular points is developed. The tree automata processed in parallel enables the realization of high-recognition speeds and font-type independent recognition. The proposed character recognition system is tested for alphanumeric character fonts of dot matrix printer and plotter using IBM-PC/XT.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.2
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• pp.8-18
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• 1993

A multi-font, multi-size and high speed character recognition system is designed. The design principles are simpilcity of algorithm, adaptibility, learnability, hierachical data processing and attention by feed back. For the multi-size character recognition, the extracted character images are normalized. A hierachical classifier classifies the feature vectors. Feature is extracted by applying the directional receptive field after the directional dege filter processing. The hierachical classifier is consist of two pre-classifiers and one decision making classifier. The effect of two pre-classifiers is prediction to the final decision making classifier. With the pre-classifiers, the time to compute the distance of the final classifier is reduced. Recognition rate is 95% for the three documents printed in three kinds of fonts, total 1,700 characters. For high speed implemention, a multiprocessor system with the ring structure of four transputers is implemented, and the recognition speed of 30 characters per second is aquired.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.19 no.11
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• pp.2133-2146
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• 1994

In this paper, we present a method for dynamic Jaso segmentation and Hangeul recognition using neural network. It uses the feature vector which is extracted from the mesh depending on the segmentation result. At first, each character is converted to 256 dimension feature vector by four direction contributivity and $8\times8$ mesh. And then, the character is classified into 6 class by neural network and is segmented into Jaso using the classification result the statistic vowel location information and the structural information. After Jaso segmentation, Hanguel recognition using neural network is performed. We experiment on four font of which three fonts are used for training the neural net and the rest is used of testing. Each font has the 2350 characters which are comprised in KS C 5601. The overall recognition rates for the training data and the testing data are 97,4% and 94&% respectively. This result shows the effectivness of proposed method.