• Journal of Korea Multimedia Society
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• v.23 no.11
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• pp.1361-1371
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• 2020

Fonts have developed as a visual element, and their influence has rapidly increased around the world. Research on font automation is actively being conducted mainly in English because Hangul is a combination character and the structure is complicated. In the previous study to solve this problem, the stroke element of the character was automatically extracted by applying the object detection by component. However, the previous research was only for similarity, so it was tested on various print style fonts, but it has not been tested on other characters. In order to extract the stroke elements of all characters and fonts, we performed a performance analysis experiment according to the expansion character in the Hangul stroke element extraction training. The results were all high overall. In particular, in the font expansion type, the extraction success rate was high regardless of having done the training or not. In the character expansion type, the extraction success rate of trained characters was slightly higher than that of untrained characters. In conclusion, for the perfect Hangul stroke element extraction model, we will introduce Semi-Supervised Learning to increase the number of data and strengthen it.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.94-105
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• 1993

In this paper propose a Koaren character recognition system using a neural network is proposed. This system is a multilayer neural network based on the masking field model which consists of a input layer, four feature extraction layers which extracts type, direction, stroke, and connection features, and an output layer which gives us recognized character codes. First, 4x4 subpatterns of an NxN character pattern stored in the input buffer are applied into the feature extraction layers sequentially. Then, each of feature extraction layers extracts sequentially features such as type, direction, stroke, and connection, respectively. Type features for direction and connection are extracted by the type feature extraction layer, direction features for stroke by the direction feature extraction layer and stroke and connection features for stroke by the direction feature extraction layer and stroke and connection features for the recongnition of character by the stroke and the connection feature extractions layers, respectively. The stroke and connection features are saved in the sequential buffer layer sequentially and using these features the characters are recognized in the output layer. The recognition results of this system by tests with 8 single consonants and 6 single vowels are promising.

• Journal of the Korean Society of Safety
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• v.8 no.2
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• pp.44-50
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• 1993

The objective of this study was to investigate the extraction of chromium(Vl) ion in waste water through the liquid surfactant membrane with open-type Perforated Reciprocating-Plate Column. Extraction experiments were conducted to measure the effect of flow characteristics of continuous and dispersed phase and stroke velocity, sodium hydroxide concentraction in internal aqueous phase, sulfuric acid concentraction in outer phase, and residence time distribution and measured extraction velocity. The result of experiments showed that extraction velocity of chromium ion was maximum when stroke velocity was 180 1/min and dispersion phase velocity was 30m11min, continuous phase velocity was 20m1/min. Extraction velocity of chromium ion increased with increasing difference of hydrogen ion concentraction of dispersion and continuous phase and column stage decreased.

• International Journal of Contents
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• v.8 no.1
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• pp.30-38
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• 2012

Text extraction and binarization are the important pre-processing steps for text recognition. The performance of text binarization strongly related to the accuracy of recognition stage. In our proposed method, the first stage based on line detection and shape feature analysis applied to locate the position of a business card and detect the shape from the complex environment. In the second stage, several local regions contained the possible text components are separated based on the projection histogram. In each local region, the pixels grouped into several connected components based on the connected component labeling and projection histogram. Then, classify each connect component into text region and reject the non-text region based on the feature information analysis such as size of connected component and stroke width estimation.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.581-584
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• 1997

Discussed is stroke identification technique for automatic recognition of kanji characters without using the order of drawing strokes of a character.

• The Transactions of the Korea Information Processing Society
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• v.1 no.3
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• pp.311-318
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• 1994

In this paper, a new stroke extraction method of Chinese character base on the human optical field(the Receptive Field of Cell) is proposed. In processing the feature extraction of the chinese character, needed are more perfect extraction methods for separated informations and its data base. This method can be applied to processing neural cell using conventional feature extraction mechanism in the optical boundary of retina and cerebrum. With this method, its applicability and effectiveness were demonstrated extracting strokes from Chinese character.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.953-964
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• 2020

Ever since media contents took over the world, the importance of typography has increased, and the influence of fonts has be n recognized. Nevertheless, the current Hangul font system is very poor and is provided passively, so it is practically impossible to understand and utilize all the shape characteristics of more than six thousand Hangul fonts. In this paper, the characteristics of Hangul font shapes were selected based on the Hangul structure of similar fonts. The stroke element detection training was performed by fine tuning Faster R-CNN Inception v2, one of the deep learning object detection models. We also propose a system that automatically extracts the stroke element characteristics from characters by introducing an automatic extraction algorithm. In comparison to the previous research which showed poor accuracy while using SVM(Support Vector Machine) and Sliding Window Algorithm, the proposed system in this paper has shown the result of 10 % accuracy to properly detect and extract stroke elements from various fonts. In conclusion, if the stroke element characteristics based on the Hangul structural information extracted through the system are used for similar classification, problems such as copyright will be solved in an era when typography's competitiveness becomes stronger, and an automated process will be provided to users for more convenience.

• Interdisciplinary Bio Central
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• v.2 no.2
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• pp.2.1-2.4
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• 2010

Complex diseases, such as stroke and cancer, have two or more genetic influences and are affected by environmental factors, which complicate them. Due to the complex characteristics of these diseases, we must search and study comprehensive literature-based article resources. Some disease-related literature databases have been developed through specialized journal issues or major websites. Most of them, however, are scattered throughout a website, and users encounter difficulties in finding accurate and comprehensive information easily and quickly. We developed StrokeMed, an integrated literature database for stroke and the differentiation of stroke syndrome. The system allows users to explore PubMed search results, categorized by MeSH (Medical Subject Headings), and the differentiation of stroke syndrome in Oriental medicine. StrokeMed collects data from important sites, such as PubMed, Scirus, and Scopus, automatically to maintain higher-quality and updated content. Currently, the system indexes more than 20,000 PubMed abstracts that are related to stroke, stroke etiology, and Oriental medicine. The system provides valuable literature information to the scientific and medical fields in stroke.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.302-306
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• 1994

This paper describes how to recognize hand written Hangeul character using the stroke order of the elementary segment. The recognition system is constructed of parts : character input part, segment disassembling part, character element extraction part and character recognition part. The character input part reads the character and performs thinning algorithm. In the segment disassembling part, the input character is disassembled into elementary segments using the direction codes and the feature parameters. In the character element extraction part, we extract the character element using the stroke order and the knowledge rule. Finally, we able to recognize the hand written Hangeul characters by assembling the character elements, in the character recognition part.

• Proceedings of the IEEK Conference
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• 2000.07b
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• pp.819-822
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• 2000

The merit of the stroke extraction algorithm is the ease of the feature abstraction from the skeleton of a character, But, extracting strokes from Korean characters has two major problems that must be dealt with. One is extracting primitive strokes and the other is merging or splitting the strokes using dynamic information of the strokes. In this paper, a method is proposed to extract strokes from an off-line handwritten Korean character. We have developed some stroke segmentation rules based on splitting, merging and directional analysis. Using these techniques, we can extract and trace the strokes in an off-line handwritten Korean character accurately and efficiently.