• Journal of Korea Multimedia Society
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• v.25 no.8
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• pp.1059-1068
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• 2022

Recently, research using image-based deep learning is being conducted to determine similar fonts or recommend fonts. In order to increase the accuracy in judging the similarity of Hangul fonts, a previous study was conducted to calculate the similarity according to the combination of stroke elements. In this study, we tried to solve this problem by designing an integrated model that reflects the weights for each stroke element. By comparing the results of the user's font similarity calculation conducted in the previous study and the weighted model, it was confirmed that there was no difference in the ranking of the influence of the stroke elements. However, as a result of comparison by letter sizes, it was confirmed that there was a difference in the ranking of the influence of stroke elements. Accordingly, we proposed a weighted model set separately for each font size.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.4
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• pp.87-94
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• 1999

This paper presents a study on the off-line handwritten Hangul (Korean) character recognition using the partially connected neural network (PCNN), which is based on partial connections between the input receptive fields and the hidden nodes. The hidden nodes of three PCNNs have ten receptive fields and different input feature sets. And we introduce modular partially connected neural network (MPCNN), The MPCNN combines three PCNNs with a merging network. The learning scheme of the proposed networks is composed of two steps: PCNN learning step and the merging step of combining three PCNN s. In the merging step, another merging PCNN network is introduced and trained by regarding the hidden output of each PCNN as a new input feature vector. The performance of the proposed classifier is verified on the recognition of 18 off-line handwritten Hangul characters widely used in business cards in Korea.

• Journal of Advanced Navigation Technology
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• v.20 no.6
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• pp.617-624
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• 2016

The hangul word embedding should be performed certainly process for noun extraction. Otherwise, it should be trained words that are not necessary, and it can not be derived efficient embedding results. In this paper, we propose model that can retrieve more efficiently by query language expansion using hangul word embedded, apriori, and text mining. The word embedding and apriori is a step expanding query language by extracting association words according to meaning and context for query language. The hangul text mining is a step of extracting similar answer and responding to the user using noun extraction, TF-IDF, and cosine similarity. The proposed model can improve accuracy of answer by learning the answer of specific domain and expanding high correlation query language. As future research, it needs to extract more correlation query language by analysis of user queries stored in database.

• Journal of IKEEE
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• v.2 no.1 s.2
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• pp.150-159
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• 1998

This paper described the recognition of the Off-line handwritten Hangul based on neural network using a feature extraction method. Features of Hangul can be extracted by a $5{\times}5$ window method which is the modified $3{\times}3$ mask method. These features are coded to binary patterns in order to use neural network's inputs efficiently. Hangul character is recognized by the consonant, the vertical vowel, and the horizontal vowel, separately. In order to verify the recognition rate, three different coding methods were used for neural networks. Three methods were the fixed-code method, the learned-code I method, and the learned-code II method. The result was shown that the learned-code II method was the best among three methods. The result of the learned-code II method was shown 100% recognition rate for the vertical vowel, 100% for the horizontal vowel, and 98.33% for the learned consonants and 93.75% for the new consonants.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.613-614
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• 2022

전 세계적인 주목을 받기 시작한 한류 콘텐츠의 확산으로 한국어 교육에 대한 수요가 폭발적으로 증가하고 있는 상황에서 효과적인 한국어 학습용 기능성 게임에 대한 가능성이 주목 받고 있다. 본 논문에서는 한국어 교육의 진입 과정에 해당 될 수 있는 한글을 게임 통해 학습하고 익숙해지는 경험을 제공할 수 있는 한글 학습 RPG '단어수집가'개발을 기획하고 해당 게임의 프로토타입을 개발한다. 제안 게임의 주인공은 가상의 공간에서의 체험을 통해 한글의 자음과 모음을 게임 아이템으로 획득하고 이를 조합하여 한글 단어를 합성하여 이를 게임에 활용하는 구조를 가지도록 설계되었고, 전체 게임 시나리오의 학습 적합성과 재미 요소들의 점검을 위해 실제 기획 의 핵심 내용을 적용한 프로토타입을 활용한다. 또한 본 게임에서 기획된 한글 학습용 게임은 미국 워싱턴에 위치한 조지워싱턴대학교에서 발행하는 미국인 대상의 한국어 교재와 스토리 등을 연동하여 온오프라인 연계 한글 교육 실현을 목표로 한다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.71-82
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• 2024

With the advancement of information and communication technology (ICT), the use of electronic document types such as PDF, MS Office, and HWP files has increased. Such trend has led the cyber attackers increasingly try to spread malicious documents through e-mails and messengers. To counter such attacks, AI-based methodologies have been actively employed in order to detect malicious document files. The main challenge in detecting malicious HWP(Hangul Word Processor) files is the lack of quality dataset due to its usage is limited in Korea, compared to PDF and MS-Office files that are highly being utilized worldwide. To address this limitation, data augmentation have been proposed to diversify training data by transforming existing dataset, but as the usefulness of the augmented data is not evaluated, augmented data could end up harming model's performance. In this paper, we propose an effective semi-supervised learning technique in detecting malicious HWP document files, which improves overall AI model performance via quantifying the utility of augmented data and filtering out useless training data.

• Journal of Computing Science and Engineering
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• v.12 no.1
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• pp.24-35
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• 2018

Deep convolutional neural network (DCNN) is an advanced technology in image recognition. Because of extreme computing resource requirements, DCNN implementation with software alone cannot achieve real-time requirement. Therefore, the need to implement DCNN accelerator hardware is increasing. In this paper, we present a field programmable gate array (FPGA)-based hardware accelerator design of DCNN targeting handwritten Hangul character recognition application. Also, we present design optimization techniques in SDAccel environments for searching the optimal FPGA design space. The techniques we used include memory access optimization and computing unit parallelism, and data conversion. We achieved about 11.19 ms recognition time per character with Xilinx FPGA accelerator. Our design optimization was performed with Xilinx HLS and SDAccel environment targeting Kintex XCKU115 FPGA from Xilinx. Our design outperforms CPU in terms of energy efficiency (the number of samples per unit energy) by 5.88 times, and GPGPU in terms of energy efficiency by 5 times. We expect the research results will be an alternative to GPGPU solution for real-time applications, especially in data centers or server farms where energy consumption is a critical problem.

• The Journal of the Korea Contents Association
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• v.7 no.6
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• pp.161-168
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• 2007

In this paper, we proposed the mathematics learning evaluation system between teachers and students using the web. The proposed web-based evaluation system lets learners make up their lesson in a self-oriented and effective way, by letting instructors diagnose learners level of understanding learned contents and letting learners take part in evaluation as well. The system also lets instructors easily make out items for evaluation by using hangul(word processor) and present them on the web. With the help of this web-based mathematics learning site and mathematics learning evaluation system, learners can perform self-oriented loaming and approach various kinds of problems. In addition, students can check with answers and have feedback on the spot. As a result, students can solve lack of understanding on the learned contents.

• Journal of Computing Science and Engineering
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• v.3 no.3
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• pp.165-180
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• 2009

The HKIB, or Hankookilbo, test collections are two archives of Korean newswire stories manually categorized with semi-hierarchical or hierarchical category taxonomies. The base newswire stories were made available by the Hankook Ilbo (The Korea Daily) for research purposes. At first, Chungnam National University and KISTI collaborated to manually tag 40,075 news stories with categories by semi-hierarchical and balanced three-level classification scheme, where each news story has only one level-3 category (single-labeling). We refer to this original data set as HKIB-40075 test collection. And then Yonsei University and KISTI collaborated to select 20,000 newswire stories from the HKIB-40075 test collection, to rearrange the classification scheme to be fully hierarchical but unbalanced, and to assign one or more categories to each news story (multi-labeling). We refer to this modified data set as HKIB-20000 test collection. We benchmark a k-NN categorization algorithm both on HKIB-20000 and on HKIB-40075, illustrating properties of the collections, providing baseline results for future studies, and suggesting new directions for further research on Korean text categorization problem.

• Korean Journal of Childcare and Education
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• v.11 no.2
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• pp.21-37
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• 2015

The purpose of this study was to evaluate the contents of Korean-learning applications by comparing two teaching methods(Phonics Instruction/Whole Language Approach). For this purpose, a total of 51 Korean-learning applications were analysed. The instruments used in this study were developed based on Applications for preschoolers Evaluation Criteria and Vocabulary Game Applications for preschoolers Evaluation Criteria. The collected data were analyzed by using a t-test. The main results are as follows. First, 'Developmental appropriateness' had the highest scores whereas 'Amusement' had the lowest scores in general. Second, there was a significant difference in 'Interaction' by teaching method. Implications for the development of more systematic Korean-learning applications for preschoolers are discussed.