• International Journal of Contents
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• v.8 no.1
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• pp.39-46
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• 2012

Character segmentation is a preprocessing step in many offline handwriting recognition systems. In this paper, Chinese characters are categorized into seven different structures. In each structure, the character size with the range of variations is estimated considering typical handwritten samples. The component removal and merge criteria are presented to remove punctuation symbols or to merge small components which are part of a character. Finally, the criteria for segmenting the adjacent characters concerning each other or overlapped are proposed.

• Smart Media Journal
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• v.10 no.1
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• pp.79-87
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• 2021

In recent years research on automatic font generation with machine learning mainly focus on using transformation-based methods, in comparison, generative model-based methods of font generation have received less attention. Transformation-based methods learn a mapping of the transformations from an existing input to a target. This makes them ambiguous because in some cases a single input reference may correspond to multiple possible outputs. In this work, we focus on font generation using the generative model-based methods which learn the buildup of the characters from noise-to-image. We propose a novel way to train a conditional generative deep neural model so that we can achieve font style control on the generated font images. Our research demonstrates how to generate new font images conditioned on both character class labels and character style labels when using the generative model-based methods. We achieve this by introducing a modified generator network which is given inputs noise, character class, and style, which help us to calculate losses separately for the character class labels and character style labels. We show that adding the character style vector on top of the character class vector separately gives the model rich information about the font and enables us to explicitly specify not only the character class but also the character style that we want the model to generate.

• ETRI Journal
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• v.32 no.5
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• pp.729-739
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• 2010

Due to the rapid development of mobile devices equipped with cameras, instant translation of any text seen in any context is possible. Mobile devices can serve as a translation tool by recognizing the texts presented in the captured scenes. Images captured by cameras will embed more external or unwanted effects which need not to be considered in traditional optical character recognition (OCR). In this paper, we segment a text image captured by mobile devices into individual single characters to facilitate OCR kernel processing. Before proceeding with character segmentation, text detection and text line construction need to be performed in advance. A novel character segmentation method which integrates touched character filters is employed on text images captured by cameras. In addition, periphery features are extracted from the segmented images of touched characters and fed as inputs to support vector machines to calculate the confident values. In our experiment, the accuracy rate of the proposed character segmentation system is 94.90%, which demonstrates the effectiveness of the proposed method.

• Proceedings of the KIEE Conference
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• 1989.07a
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• pp.477-480
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• 1989

In this paper, we introduce the three-dimensional Korean character display by using 2D fonts and character depth. Character segments are designed by the set of vertex at run time. Character depth is applied for 3D visualization. And, the variation of eye-point and distance of object is used for 3D character animation.

• Journal of information and communication convergence engineering
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• v.7 no.3
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• pp.345-350
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• 2009

Hangul search functionality, including dictionary search is used in many Hangeul applications. Existing research of hangeul search method is the study of using hangeul syllable as a basic unit. However when you consider the characteristics of Hangul, the research of using hangeul character as a basic unit is needed. In this paper we propose the character based hangeul search method using the location-specific frequency information and verify the effectiveness of the proposed method through the experiments.

• International Journal of Contents
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• v.8 no.3
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• pp.83-93
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• 2012

In this paper, we propose a novel method that automatically generates real character images to familiarize existing OCR systems with new fonts. At first, we generate synthetic character images using a simple degradation model. The synthetic data is used to train an OCR engine, and the trained OCR is used to recognize and label real character images that are segmented from ideal document images. Since the OCR engine is unable to recognize accurately all real character images, a substring matching method is employed to fix wrongly labeled characters by comparing two strings; one is the string grouped by recognized characters in an ideal document image, and the other is the ordered string of characters which we are considering to train and recognize. Based on our method, we build a system that automatically generates 2350 most common Korean and 117 alphanumeric characters from new fonts. The ideal document images used in the system are postal envelope images with characters printed in ascending order of their codes. The proposed system achieved a labeling accuracy of 99%. Therefore, we believe that our system is effective in facilitating the generation of numerous character samples to enhance the recognition rate of existing OCR systems for fonts that have never been trained.

• Proceedings of the Korean Society of Computer Information Conference
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• 2013.01a
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• pp.29-30
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• 2013

본 논문에서는 영화나 소설과 같은 콘텐츠의 스토리를 가시화해서 보여주는 시스템에 대해 제안하고 설명한다. 스토리를 가시화 해주기 위해 등장인물들 간의 관계를 모형화하는 Character-net 방법론을 채용하였고 스토리 진행에 따른 Character-net 변화를 분석하여 보여주는 시스템을 개발하였다. 시스템은 Character-net 변화 실행창과 등장인물 중심성 시계열 그래프 창으로 구성하였다. 두 개 창을 통해 스토리 차원의 검색이 가능토록 하였다. 본 논문에서는 스토리 가시화 시스템에 대해 설명하고 추가적으로 필요한 사항들에 대해 논의한다.

• Journal of the Korea Computer Industry Society
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• v.2 no.3
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• pp.285-296
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• 2001

This Paper develops the Hanguel character learning system for elementary school students and foreigners. Standard character pattern is selected and DB is consructed for model by feature extraction. For this, performance of pre-processing independent of environments, feature extraction and simility functions are defined. Finally, beauty evaluation is done by matching between input character pattern written by elementary school students or foreigners and standard character pattern. It is possible for this system to extend the specific character font learning from selecting the specific standard character pattern. Also the effectiveness of this parer is demonstrated by several experiments.

• Journal of the Korea Society of Computer and Information
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• v.24 no.4
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• pp.45-50
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• 2019

Recently, character motion have been used extensively in the entertainment business, and researchers have investigated algorithms of reproducing, editing, and simulating mimic human movements. Also, many recent researches have suggested how a character interacts with its surrounding environment in terms of motion. Specially, spatial relationships of the environment have been introduced for adapting and preserving character motion. In this paper, we propose a motion adaptation technique preserving a spatial property between a virtual character and the configuration of its surrounding space. Additionally, we report on experimental results of smoothly adapted motions in various environmental structures with original motions such as walk, jump, and tumbling.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.5 no.1
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• pp.21-25
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• 2004

This paper presents a typographical analyses and classes. Typographical analysis is an indispensable tool for machine-printed character recognition in English. This analysis is a preliminary step for character segmentation in OCR. This paper is divided into two parts. In the first part, word typographical classes from words are defined by the word typographical analysis. In the second part, character typographical classes from connected components are defined by the character typographical analysis. The character typographical classes are used in the character segmentation.