• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권2호
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• pp.722-733
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• 2012

Recently various studies on various applications using images taken by mobile phone cameras have been actively conducted. This study proposes a correction method for misrecognition of Korean Texts in signboard images using improved Levenshtein metric. The proposed method calculates distances of five recognized candidates and detects the best match texts from signboard text database. For verifying the efficiency of the proposed method, a database dictionary is built using 1.3 million words of nationwide signboard through removing duplicated words. We compared the proposed method to Levenshtein Metric which is one of representative text string comparison algorithms. As a result, the proposed method based on improved Levenshtein metric represents an improvement in recognition rates 31.5% on average compared to that of conventional methods.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2016년도 추계학술대회
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• pp.194-196
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• 2016

본 논문에서는 안드로이드 환경에서 글자 인식을 위한 전처리 과정으로 입력 영상에서 글자 영역만을 추출하는 기법을 제안한다. 대부분의 글자 인식 어플리케이션에서 글자를 인식하는 방법은 RoI(Region of Interest)에 인식하려는 글자를 위치시켜 놓고 사용자가 촬영함으로써 진행된다. 하지만 촬영된 영상 그대로를 인식에 사용하기 때문에 잡음 및 글자가 아닌 영역들을 글자로 인식하는 문제 등으로 인하여 인식률이 현저히 떨어진다. 제안하는 기법에서는 MSER(Maximally Stable Extremal Regions) 기법을 통해 각각의 글자를 추출한 후, 글자의 특성을 이용하여 글자 영역만을 추출한다. 기법의 성능 평가는 무료 OCR(Optical Character Recognition) 엔진인 Tesseract-OCR을 통해 글자 인식률을 비교하였으며, 제안하는 기법을 적용한 글자 인식 시스템이 적용하지 않은 시스템보다 글자의 인식률이 향상되는 것을 확인하였다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 1999년도 KOBA 방송기술 워크샵 KOBA Broadcasting Technology Workshop
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• pp.151-158
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• 1999

This paper describes TVML (TV program Making Language) for automatically generating television programs from text-based script. This language describes the contents of a television program using expression with a high level of abstraction like“title #1”and“zoom-in”. The software used to read a script written in TVML and to automatically generate the program video and audio is called the TVML Player. The paper begins by describing TVML language specifications and the TVML Player. It then describes the“external control mode”of the TVML Player that can be used for applying TVML to interactive applications. Finally, it describes the TVML Editor, a user interface that we developed which enables users having no specialized knowledge of computer languages to make TVML scripts. In addition to its role as a television-program production tool. TVML is expected to have a wide range of applications in the network and multimedia fields.

• 한국산학기술학회논문지
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• 제12권7호
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• pp.2957-2962
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• 2011

본 논문은 텔레비전 프로그램에서 방송되고 있는 데이트 쇼 프로그램을 기호학적으로 분석하고, 다양한 사회문화적 현상이 담고 있는 특성을 살펴보는데 목적을 갖고 있다. 본 연구의 분석대상인 데이트 쇼 프로그램은 케이블 채널 TVn의 <러브 스위치>의 텍스트 분석"을 통해서 젠더의 구분에 따른 특정한 사고방식과 행위방식에서 드러나고 있는 남녀의 구조적 특성을 살펴보고, 텔레비전 문화가 어떻게 우리의 의식세계를 지배하고 발전되고 있는지를 고찰하고자 한다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2019년도 하계학술대회
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• pp.80-81
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• 2019

본 논문에서는 자연스러운 장면 영상에서 임의의 방향성을 가진 텍스트를 검출하기 위한 기법을 제안한다. 텍스트 검출을 위한 기본적인 프레임 워크는 Faster R-CNN[1]을 기반으로 한다. 먼저 RPN(Region Proposal Network)을 통해 다른 방향성을 가진 텍스트를 포함하는 bounding box를 생성한다. 이어서 RPN에서 생성한 각각의 bounding box에 대해 세 가지의 서로 다른 크기로 pooling된 특징지도를 추출하고 병합한다. 병합한 특징지도에서 텍스트와 텍스트가 아닌 대상에 대한 score, 정렬된 bounding box 좌표, 기울어진 bounding box 좌표를 모두 예측한다. 마지막으로 NMS(Non-Maximum Suppression)을 이용하여 검출 결과를 획득한다. COCO Text 2017 dataset[2]을 이용하여 학습 및 테스트를 진행하였으며 주관적으로 평가한 결과 기울어진 텍스트에 적합하게 회전된 영역을 얻을 수 있음을 확인하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.676-691
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• 2022

An increasing number of companies in the fashion industry are using AI curation services. The purpose of this study is to investigate perceptions of and intentions to use AI fashion curation services among customers by using text mining. To accomplish this goal, we collected a total of 34,190 online posts from two Korean portals, Naver and Daum. We conducted frequency analysis to identify the most frequently mentioned keywords using Textom. The analysis extracted "various," "good," "many," "right," and "new" at the highest frequency, indicating that consumers had positive perceptions of AI fashion curation services. In addition, we conducted a semantic network analysis with the top-50 most frequently used keywords, classifying customers' perceptions of AI fashion curation services into three groups: shopping, platform, and business profit. We also identified the factors that boost continuous use intentions: usability, usefulness, reliability, enjoyment, and personalization. We conclude this paper by discussing the theoretical and practical implications of these findings.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2022년도 추계학술대회
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• pp.72-75
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• 2022

야외 환경을 카메라로 촬영한 일반 영상에서 텍스트 이미지를 검출하고 인식하는 기술은 로봇 비전, 시각 보조 등의 기반이 되는 기술로 활용될 수 있어 매우 중요한 기술이다. 하지만 저해상도의 텍스트 이미지의 경우 텍스트 이미지에 포함된 노이즈나블러 등이 더 두드러지기 때문에 텍스트 내용을 인식하는 것이 어렵다. 이에 본 논문은 일반 영상에서의 저해상도 한글 및 영어 텍스트에 대한 이미지 초해상화를 통해 텍스트 인식 정확도를 개선하였다. 트랜스포머에 기반한 모델로 한글 및 영어 텍스트에 대한 이미지 초해상화를 수행하였으며, 영어 및 한글 데이터셋에 대해 제안한 초해상화 방법을 적용했을 때 그렇지 않을 때보다 텍스트 인식 성능이 개선되는 것을 확인하였다.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.101-109
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• 2023

This paper discusses the use of sentiment analysis and text summarization techniques to extract valuable information from the large volume of user-generated content such as reviews, comments, and feedback on online platforms and social media. The paper highlights the effectiveness of sentiment analysis in identifying positive and negative reviews and the importance of summarizing such text to facilitate comprehension and convey essential findings to readers. The proposed work focuses on summarizing all positive and negative reviews to enhance product quality, and the performance of the generated summaries is measured using ROUGE scores. The results show promising outcomes for the developed methods in summarizing user-generated content.

• Journal of Information Technology Applications and Management
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• 제28권5호
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• pp.41-50
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• 2021

As COVID-19 spread widely, and rapidly, the number of misinformation is also increasing, which WHO has referred to this phenomenon as "Infodemic". The purpose of this research is to develop detection and rectification of COVID-19 misinformation based on Open-domain QA system and text similarity. 9 testing conditions were used in this model. For open-domain QA system, 6 conditions were applied using three different types of dataset types, scientific, social media, and news, both datasets, and two different methods of choosing the answer, choosing the top answer generated from the QA system and voting from the top three answers generated from QA system. The other 3 conditions were the Closed-Domain QA system with different dataset types. The best results from the testing model were 76% using all datasets with voting from the top 3 answers outperforming by 16% from the closed-domain model.

• 한국안전학회지
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• 제31권6호
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• pp.121-128
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• 2016

Social Media transformed the mass media based information traffic, and it has become a key resource for finding value in enterprises and public institutions. Particularly, in regards to disaster management, the necessity for public participation policy development through the use of social media is emphasized. National Disaster Management Research Institute developed the Social Big Board, which is a system that monitors social Big Data in real time for purposes of implementing social media disaster management. Social Big Board collects a daily average of 36 million tweets in Korean in real time and automatically filters disaster safety related tweets. The filtered tweets are then automatically categorized into 71 disaster safety types. This real time tweet monitoring system provides various information and insights based on the tweets, such as disaster issues, tweet frequency by region, original tweets, etc. The purpose of using this system is to take advantage of the potential benefits of social media in relations to disaster management. It is a first step towards disaster management that communicates with the people that allows us to hear the voice of the people concerning disaster issues and also understand their emotions at the same time. In this paper, Korean language text mining based Social Big Board will be briefly introduced, and disaster issue detection model, which is key algorithms, will be described. Disaster issues are divided into two categories: potential issues, which refers to abnormal signs prior to disaster events, and occurrence issues, which is a notification of disaster events. The detection models of these two categories are defined and the performance of the models are compared and evaluated.