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Proposal for Deep Learning based Character Recognition System by Virtual Data Generation (가상 데이터 생성을 통한 딥러닝 기반 문자인식 시스템 제안)

  • Lee, Seungju;Park, Gooman
    • Journal of Broadcast Engineering
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    • v.25 no.2
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    • pp.275-278
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    • 2020

  • In this paper, we proposed a deep learning based character recognition system through virtual data generation. In order to secure the learning data that takes the largest weight in supervised learning, virtual data was created. Also, after creating virtual data, data generalization was performed to cope with various data by using augmentation parameter. Finally, the learning data composition generated data by assigning various values to augmentation parameter and font parameter. Test data for measuring the character recognition performance was constructed by cropping the text area from the actual image data. The test data was augmented considering the image distortion that may occur in real environment. Deep learning algorithm uses YOLO v3 which performs detection in real time. Inference result outputs the final detection result through post-processing.

  • https://doi.org/10.5909/JBE.2020.25.2.275 인용 PDF KSCI KPUBS

The Study on Data Governance Research Trends Based on Text Mining: Based on the publication of Korean academic journals from 2009 to 2021 (텍스트 마이닝을 활용한 데이터 거버넌스 연구 동향 분석: 2009년~2021년 국내 학술지 논문을 중심으로)

  • Jeong, Sun-Kyeong
    • Journal of Digital Convergence
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    • v.20 no.4
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    • pp.133-145
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    • 2022

  • As a result of the study, the poorest keywords were information, big data, management, policy, government, law, and smart. In addition, as a result of network analysis, related research was being conducted on topics such as data industry policy, data governance performance, defense, governance, and data public. The four topics derived through topic modeling were "DG policy," "DG platform," "DG in laws," and "DG implementation," of which research related to "DG platform" showed an increasing trend, and "DG implementation" tended to shrink. This study comprehensively summarized data governance-related studies. Data governance needs to expand research areas from various perspectives and related fields such as data management and data integration policies at the organizational level, and related technologies. In the future, we can expand the analysis targets for overseas data governance and expect follow-up studies on research directions and policy directions in industries that require data-based future industries such as Industry 4.0, artificial intelligence, and Metaverse.

  • https://doi.org/10.14400/JDC.2022.20.4.133 인용 PDF KSCI

Development of Hybrid Recommender System Using Review Data Mining: Kindle Store Data Analysis Case (리뷰 데이터 마이닝을 이용한 하이브리드 추천시스템 개발: Amazon Kindle Store 데이터 분석사례)

  • Yihua Zhang;Qinglong Li;Ilyoung Choi;Jaekyeong Kim
    • Information Systems Review
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    • v.23 no.1
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    • pp.155-172
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    • 2021

  • With the recent increase in online product purchases, a recommender system that recommends products considering users' preferences has still been studied. The recommender system provides personalized product recommendation services to users. Collaborative Filtering (CF) using user ratings on products is one of the most widely used recommendation algorithms. During CF, the item-based method identifies the user's product by using ratings left on the product purchased by the user and obtains the similarity between the purchased product and the unpurchased product. CF takes a lot of time to calculate the similarity between products. In particular, it takes more time when using text-based big data such as review data of Amazon store. This paper suggests a hybrid recommendation system using a 2-phase methodology and text data mining to calculate the similarity between products easily and quickly. To this end, we collected about 980,000 online consumer ratings and review data from the online commerce store, Amazon Kinder Store. As a result of several experiments, it was confirmed that the suggested hybrid recommendation system reflecting the user's rating and review data has resulted in similar recommendation time, but higher accuracy compared to the CF-based benchmark recommender systems. Therefore, the suggested system is expected to increase the user's satisfaction and increase its sales.

  • https://doi.org/10.14329/isr.2021.23.1.155 인용 PDF