• International journal of advanced smart convergence
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• v.8 no.3
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• pp.131-137
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• 2019

This study proposes a method of learning and recognizing the characteristics that are the classification criteria of Hangul using Mask R-CNN, one of the deep learning techniques, to recognize and classify atypical Hangul characters. The atypical characters on the Hangul signboard have a lot of deformed and colorful shapes beyond the general characters. Therefore, in order to recognize the Hangul signboard character, it is necessary to learn a separate atypical Hangul character rather than the existing formulaic one. We selected the Hangul character '닭' as sample data and constructed 5,383 Hangul image data sets and used them for learning and verifying the deep learning model. The accuracy of the results of analyzing the performance of the learning model using the test set constructed to verify the reliability of the learning model was about 92.65% (the area detection rate). Therefore we confirmed that the proposed method is very useful for Hangul signboard character recognition, and we plan to extend it to various Hangul data.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.27 no.2
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• pp.135-145
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• 2023

Recently, as deep learning technology has developed, various deep learning technologies have been introduced in handwritten recognition, greatly contributing to performance improvement. The recognition accuracy of handwritten Hangeul recognition has also improved significantly, but prior research has focused on recognizing 520 Hangul characters or 2,350 Hangul characters using SERI95 data or PE92 data. In the past, most of the expressions were possible with 2,350 Hangul characters, but as globalization progresses and information and communication technology develops, there are many cases where various foreign words need to be expressed in Hangul. In this paper, we propose a model that recognizes and combines the consonants, medial vowels, and final consonants of a Korean syllable using a multi-label classification model, and achieves a high recognition accuracy of 98.38% as a result of learning with the public data of Korean handwritten characters, PE92. In addition, this model learned only 2,350 Hangul characters, but can recognize the characters which is not included in the 2,350 Hangul characters

• Proceedings of the KIEE Conference
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• 1990.11a
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• pp.465-469
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• 1990

This thesis proposes a new scheme for the recognition of presegmented Hangul characters. The proposed approach is rather insensitive to noise and variation by applying 2 dimensional convolution to learning patterns. In this thesis, the hangul recognition neural network is implemented in the basis of this scheme and recognition rate is analyzed in boo cases of learning which are learning by binary patterns and learning by binary patterns and convoluted patterns together.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.850-861
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• 2020

Hangul is a language that is composed of initial, medial, and final syllables. It has 11,172 characters. For this reason, the current method of designing all the characters by hand is very expensive and time-consuming. In order to solve the problem, this paper proposes an automatic Hangul font generation system and evaluates the standards for mapping Hangul characters to produce an effective automated Hangul font generation system. The system was implemented using character generation engine based on deep learning CycleGAN. In order to evaluate the criteria when mapping characters in pairs, each criterion was designed based on Hangul structure and character shape, and the quality of the generated characters was evaluated. As a result of the evaluation, the standards designed based on the Hangul structure did not affect the quality of the automated Hangul font generation system. On the other hand, when tried with similar characters, the standards made based on the shape of Hangul characters produced better quality characters than when tried with less similar characters. As a result, it is better to generate automated Hangul font by designing a learning method based on mapping characters in pairs that have similar character shapes.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.12
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• pp.499-508
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• 2022

A lot of research has been carried out on the Hangeul generation model using deep learning, and recently, research is being carried out how to minimize the number of characters input to generate one set of Hangul (Few-Shot Learning). In this paper, we propose a CKFont2 model using only 14 letters by analyzing and improving the CKFont (hereafter CKFont1) model using 28 letters. The CKFont2 model improves the performance of the CKFont1 model as a model that generates all Hangul using only 14 characters including 24 components (14 consonants and 10 vowels), where the CKFont1 model generates all Hangul by extracting 51 Hangul components from 28 characters. It uses the minimum number of characters for currently known models. From the basic consonants/vowels of Hangul, 27 components such as 5 double consonants, 11/11 compound consonants/vowels respectively are learned by deep learning and generated, and the generated 27 components are combined with 24 basic consonants/vowels. All Hangul characters are automatically generated from the combined 51 components. The superiority of the performance was verified by comparative analysis with results of the zi2zi, CKFont1, and MX-Font model. It is an efficient and effective model that has a simple structure and saves time and resources, and can be extended to Chinese, Thai, and Japanese.

• Cartoon and Animation Studies
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• s.35
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• pp.291-312
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• 2014

This study is based on the theory of R.Caillois about element of play which is motivated to infant for studying Hangul. The ultimate goal of play has to be accompanied by pleasure. And learning means permanent changes from experiences for the individual's. Play and learning, these two elements are united to the genre of serious game since the GBL (game based learning) was lead. Most importantly, in order to achieve their own Hangul learning is the fun. Coupled with fun and learning has an important issue for flow because concentration is low in infants than adults. In this case study is to know about fun factor has been applied effectively to Hangul serious mobile game. 20 Infant Hangul mobile serious games of Google Android mobile game section were selected as a case study based on more than 10,000 downloads and user's review rate by April 22, 2014. After that is currently available on the market can play a variety of cases of infant learning Hangul from previous research of R.Caillois offers four categories of play. R.Caillois of Agon, Mimicry, Alea, Ilinx have unique characteristics in comparison with its functional characteristics Hangul four are present any role in Hangul serious mobile games. As a result of the cases selected and the rules of the game will include a maximum of two of the most common types of Agon. Each attribute of the play, rather than one single factor is applied to four kinds of game play performance when properties are distributed to experience together gave the best flow. As a result of this study will be a based research for infants Hangul serious mobile game reflects the properties of the elements of a fun game that you want to combine learning.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.76-80
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• 1993

In this paper we discuss optimization of neural network parameters, such as inclination of the sigmoid function, the numbers of the input layer's units and the hidden layer's units, considering application to recognition of hand written Hangul. Hangul characters are composed of vowels and consonants, and basically classified to six patterns by their positions. Using these characteristics of Hangul, the pattern of a given character is determined by its peripheral distribution and the other features. After then, the vowels and the consonants are recognized by the optimized neural network. The constructed recognition system including a neural network is applied to non-learning Hangul written by some Korean people, which are the names randomly taken from Korean spiritual and cultural research institute.

• 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.

• The Journal of the Korea Contents Association
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• v.16 no.2
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• pp.449-455
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• 2016

Times have changed and using smart phones is common to everyone, regardless of men and women of all ages. Especially, making educational applications is on a rising trend. This report aims to build a program, which makes young children easily meet and learn Hangul anytime, anywhere, to keep pace with the change and trend. As everyone knows, young children's Hangul learning process is changing with smart phone generalization. Young children usually learned Hangul based on paper books but now times have changed, instead, smart phones can be used to learn Hangul anytime, anywhere, and this trend is seen everywhere easily. This report explains how to build smart phone applications to make it easier to learn and understand educational Hangul plays.

• The Journal of Information Technology
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• v.9 no.1
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• pp.55-63
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• 2006

In this paper, a Hangul recognition system using of Kohonen Network in the phoneme separation and learning is proposed. A Hangul consists of phoneme that are consists of strokes. The phoneme recognition and separation are very important in the recognition of character. So, the phonemes which mismatching has been happened are correctly separated through the learning of neural networks. also, learning rate($\alpha$) adjusted according to error, in order to solved that its decreased the number of iteration and the problem of local minimum, adaptively.