• Journal of KIISE:Software and Applications
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• v.28 no.7
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• pp.511-523
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• 2001

필기 한글의 문자나 단어 인식에 있어 패턴을 구성하는 획 성분을 추출하는 작업은 매우 중요하다. 세선화와 직선근사에 기반한 고전적인 방법은 원래 패턴을 크게 왜곡한다는 단점을 가지고 있다. 우리는 이러한 문제점을 해결하기 위하여 한글 패턴에 적합한 모양 분해 알고리즘을 제안한다. 한글 패턴은 T-접점과 B-접점이라는 두가지 모양 특징을 중심으로 분할할 수 있다고 관찰에 근거하여 알고리즘을 설계하였다. 또한 세 개 이상의 획이 복잡한 형태로 만나는 결합 지점을 강전하게 처리하는 방법도 제시한다. 제안한 알고리즘을 PE92 데이터베이스에 적용한 결과를 제시한다.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.53-56
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• 2003

In this paper, we propose a new method which generates the trajectory of the mouth shape for the characters by the user inputs. It is based on the character at a basis syllable and can be suitable to the mouth shape generation. In this paper, we understand the principle of the Korean language creation and find the similarity for the form of the mouth shape and select it as a basic syllable. We also consider the articulation of this phoneme for it and create a new mouth shape trajectory and apply at face of an 3D avatar.

• Proceedings of the KIEE Conference
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• 1991.11a
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• pp.201-204
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• 1991

In this paper the method of representing the document image by the quadtree data structure, and extracting each character seperately from the constructed quadtree are described. The document image is represented by a binary encoded quadtree and the segmentation is performed according to the information of each leaf node of the quadtree. Then, each character is extracted by the relation of positions of segments. This method enables to extract characters without examining every pixel in the image and the required storage of document image is decreased.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.84-93
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• 1993

Recently, several nonlinear shape normalization methods have been proposed in order to compensate for the shape distortions in handwritten characters. In this paper, we review these nonlinear shape normalization methods from the two points of view : feature projection and feature density equalization. The former makes feature projection histogram by projecting a certain feature at each point of input image into horizontal-or vertical-axis and the latter equalizes the feature densities of input image by re-sampling the feature projection histogram. A systematic comparison of these methods has been made based on the following criteria: recognition rate, processing speed, computational complexity and measure of variation. Then, we present the result of quantitative evaluation of each method based on these criteria for a large variety of handwritten Hangul syllables.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7A
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• pp.581-585
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• 2012

This paper proposes an adaptive binarization algorithm which is highly effective for a degraded document image including printed Hangul and Chinese characters. Because of the attribute of character composed of thin horizontal strokes and thick vertical strokes, the conventional algorithms can't easily extract horizontal strokes which have weaker components than vertical ones in the degraded document image. The proposed algorithm solves the conventional algorithm's problem by adding a vertical-directional reference adaptive binarization algorithm to an omni-directional reference one. The simulation results show the proposed algorithm extracts well characters from various degraded document images.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.4
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• pp.1166-1186
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• 2022

Artistic font design has become an integral part of visual media. However, without prior knowledge of the font domain, it is difficult to create distinct font styles. When the number of characters is limited, this task becomes easier (e.g., only Latin characters). However, designing CJK (Chinese, Japanese, and Korean) characters presents a challenge due to the large number of character sets and complexity of the glyph components in these languages. Numerous studies have been conducted on automating the font design process using generative adversarial networks (GANs). Existing methods rely heavily on reference fonts and perform font style conversions between different fonts. Additionally, rather than capturing style information for a target font via multiple style images, most methods do so via a single font image. In this paper, we propose a network architecture for generating multilingual font sets that makes use of geometric structures as content. Additionally, to acquire sufficient style information, we employ multiple style images belonging to a single font style simultaneously to extract global font style-specific information. By utilizing the geometric structural information of content and a few stylized images, our model can generate an entire font set while maintaining the style. Extensive experiments were conducted to demonstrate the proposed model's superiority over several baseline methods. Additionally, we conducted ablation studies to validate our proposed network architecture.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.2
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• pp.73-78
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• 2021

Recently, as interest in deep learning has increased, many researches in various fields using deep learning techniques have been conducted. Studies on automatic generation of fonts using deep learning-based generation models are limited to several languages such as Roman or Chinese characters. Generating Korean font is a very time-consuming and expensive task, and can be easily created using deep learning. For research on generating Korean fonts, it is important to prepare a Korean font dataset from the viewpoint of process automation in order to keep pace with deep learning-based generation models. In this paper, we propose a Korean font dataset for deep learning-based Korean font research and describe a method of constructing the dataset. Based on the Korean font data set proposed in this paper, we show the usefulness of the proposed dataset configuration through the process of applying it to a deep learning Korean font generation application.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.7
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• pp.42-49
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• 1989

A new integrated coding method is proposed in this study for embedding the text information including Hangul into an image. A monochrome analog image may be quantized to a few leveled digital image and be displayed on bi-leveled output devices by using halftone processing techniques. Text data are embedded on each micro pattern. Based on this concept, the encoding and the decoding algorithm are implemented and experiments are performed. As a result, the average amount of the embedded text information is more than 8 bpp (bits per pixer) in this halftone processed image converted form a $64{\times}64$ image, i.e, corresponding to 2000 characters in Hangul, or 4000 characters in alphanumeral. using this algorithm, the integrated personal record management system is implemented.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.28B no.11
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• pp.852-858
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• 1991

An adaptive hierarchical classifier(AHCL) for Korean character recognition using a neural net is designed. This classifier has two neural nets: USACL (Unsupervised Adaptive Classifier) and SACL (Supervised Adaptive Classifier). USACL has the input layer and the output layer. The input layer and the output layer are fully connected. The nodes in the output layer are generated by the unsupervised and nearest neighbor learning rule during learning. SACL has the input layer, the hidden layer and the output layer. The input layer and the hidden layer arefully connected, and the hidden layer and the output layer are partially connected. The nodes in the SACL are generated by the supervised and nearest neighbor learning rule during learning. USACL has pre-attentive effect, which perform partial search instead of full search during SACL classification to enhance processing speed. The input of USACL and SACL is a directional edge feature with a directional receptive field. In order to test the performance of the AHCL, various multi-font printed Hangul characters are used in learning and testing, and its processing its speed and and classification rate are compared with the conventional LVQ(Learning Vector Quantizer) which has the nearest neighbor learning rule.

• Korean Journal of Cognitive Science
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• v.27 no.2
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• pp.325-353
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• 2016

The visual span refers to the number of letters that can be accurately recognized without moving one's eyes. The size of the visual span is affected by sensory factors such as perimetric complexity, crowding, and mislocation of letters. Korean Hangul utilizes rather unique alphabetic-syllabary writing system, quite different from English and Chinese writing systems. Due to this combinatorial nature of the script, the visual span for Hangul characters can also be affected by the letter type (e.g., CV vs CVCC). The present study examined the effect of syllable complexity on the visual span for Hangul by comparing letter recognition accuracy across four letter type conditions (C only, CV, CVC, and CVCC). We also aimed to determine the meaningful letter type(s) that is associated with differences in reading abilities in Korean. Using a trigram presentation method, we found that overall recognition accuracy declined as syllable complexity increased. However, the visual span for CVC type was greater than that for CV type, suggesting that the effect is not necessarily linear, and that there might be other factors affecting the visual span for these types of letters. C and CV type showed fairly strong positive correlations with reading comprehension, suggesting that these might be the meaningful units for measuring visual span in relating to reading abilities.