• Journal of Digital Contents Society
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• v.12 no.1
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• pp.57-67
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• 2011

Recently, an interest in a mobile search is increasing according to the growing propagation of smart phones. However, a keypad, which is not appropriate for mobile environment, is the only input media for the mobile search. As an alternative, voice emerged as a new media for the mobile search, but this also has weaknesses. Thus, in the paper, we propose a mobile content called Orthros for searching the Internet using images as an input. Orthros extracts texts from images, and then inserts the texts to public search engines as a keyword. Also, Orthros can repeat searching with the extracted texts by storing result URL to internal databases. As an experiment, we analyze properties of recognizable images and present the implementation method in details.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.6
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• pp.103-110
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• 2016

The recent frequent cases of damage due to leakage of medical data and the privacy of medical patients is increasing day by day. The government says the Privacy Rule regulations established for these victims, such as prevention. Medical data guidelines can be seen 'national medical privacy guidelines' is only released. When replacing the image data between the institutions it has been included in the image file (JPG, JPEG, TIFF) there is exchange of data in common formats such as being made when the file is leaked to an external file there is a risk that the exposure key identification information of the patient. This medial image file has no protection such as encryption, This this paper, introduces a masking technique using a mosaic technique encrypting the image file contains the application to optical character recognition techniques. We propose pseudonymization technique of personal information in the image data.

• The Journal of Information Systems
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• v.26 no.3
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• pp.273-293
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• 2017

Purpose The purpose of this study is to introduce a framework that helps Muslims to determine whether a food can be consumed. It can complement existing Halal food classification services having a difficulty of constructing Halal food database. Design/methodology/approach The proposed framework includes two components. First, OCR(Optical Character Recognition) technique is utilized to read the food additive information. Second, machine learning methods were used to trained and predicted to determine whether a food can be consumed using the provided information. Findings Among the compared machine learning methods, SVM(Support Vector Machine), DT(Decision Tree), and NB(Naive Bayes), SVM with linear kernel and DT had excellent performance in the Halal food classification. The framework which adopting the proposed framework will enhance the tourism experiences of Muslim tourists who consider keeping the Islamic law most importantly. Furthermore, it can eventually contribute to the enhancement of smart tourism ecosystem.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.24-29
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• 2023

In this paper, we propose a real-time bus arrival notification system using deep learning to guarantee movement rights for the visually impaired. In modern society, by using location information of public transportation, users can quickly obtain information about public transportation and use public transportation easily. However, since the existing public transportation information system is a visual system, the visually impaired cannot use it. In Korea, various laws have been amended since the 'Act on the Promotion of Transportation for the Vulnerable' was enacted in June 2012 as the Act on the Movement Rights of the Blind, but the visually impaired are experiencing inconvenience in using public transportation. In particular, from the standpoint of the visually impaired, it is impossible to determine whether the bus is coming soon, is coming now, or has already arrived with the current system. In this paper, we use deep learning technology to learn bus numbers and identify upcoming bus numbers. Finally, we propose a method to notify the visually impaired by voice that the bus is coming by using TTS technology.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1353-1356
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• 2021

블록체인은 다수의 노드 네트워크 내에서 거래내역을 분산 저장함으로써 투명성을 확보하는 기술이다. 최근에는 금전적 가치를 지닌 선하증권(Bill of Lading, B/L 서류)에 블록체인을 적용하여 무결성을 확보하고 거래 과정을 간소화 하기위한 연구가 진행되고 있다. 본 논문에서는 거래자 중심의 블록체인 기반의 선하증권 시스템을 제안한다. 수출자는 발행 받은 선하증권을 AI(Artificial intelligence)기반의 OCR(Optical character recognition)기능을 통해 블록체인에 등록하고, 각국 은행에서 열람하여 신용장거래를 진행한다. 수입자는 선하증권 정보를 담은 QR(Quick Response code)코드로 자기증명을 하여 물품을 인도 받게 된다. 이는 수출자 측에서는 선적서류를 우편으로 보낼 시간과 비용을 단축하고, 서류의 무결성을 입증할 수 있다는 점에서 큰 효과를 얻을 수 있다. 수입자 측에서는 서류가 등록됨과 동시에 확인할 수 있고, 해당 거래를 신뢰할 수 있다는 이점을 갖는다. 마지막으로 은행 측에서는 선적서류에 대해 보안성을 갖출 수 있고 검증이 더 신속하게 이루어질 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.11a
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• pp.673-675
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• 2022

우리 사회에서 개인이 복용하고 있는 약물의 종류와 수가 점점 늘어나고 있다. 약물의 사용이 증가하면서 때로는 치명적일 수 있는 약물 오남용 또한 빈번히 발생하고 있으며 특히 노년층과 같이 약품을 정확하게 구별할 수 없는 사람들은 더욱더 그 위험에 노출되어있다. 본 논문에서는 사용자가 간단한 사진을 찍는 행위를 거치면 약물의 정보를 제공하고, 복용법을 알 수 있는 모바일 애플리케이션에 관하여 기술한다. 이를 구현하기 위하여 세밀한 시각적 분류 (Fine-Grained Visual Categorization, FGVC) 기법과 광학 문자 인식 (Optical Character Recognition, OCR) 기법을 결합한 인공지능 모델을 사용하였으며, React Native 를 사용하여 운영체제에 종속되지 않도록 애플리케이션을 제안한다. 이 애플리케이션은 노년층에 친화된 UI/UX 로 디자인되었으며, 약물의 정보 제공 이외에도 개인 약물 관리, 주변 약국 길 찾기 등의 편의 기능을 통해 노년층에 삶의 질 향상을 기대할 수 있을 것이다.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.1-23
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• 2018

From the 21st century, various high-quality services have come up with the growth of the internet or 'Information and Communication Technologies'. Especially, the scale of E-commerce industry in which Amazon and E-bay are standing out is exploding in a large way. As E-commerce grows, Customers could get what they want to buy easily while comparing various products because more products have been registered at online shopping malls. However, a problem has arisen with the growth of E-commerce. As too many products have been registered, it has become difficult for customers to search what they really need in the flood of products. When customers search for desired products with a generalized keyword, too many products have come out as a result. On the contrary, few products have been searched if customers type in details of products because concrete product-attributes have been registered rarely. In this situation, recognizing texts in images automatically with a machine can be a solution. Because bulk of product details are written in catalogs as image format, most of product information are not searched with text inputs in the current text-based searching system. It means if information in images can be converted to text format, customers can search products with product-details, which make them shop more conveniently. There are various existing OCR(Optical Character Recognition) programs which can recognize texts in images. But existing OCR programs are hard to be applied to catalog because they have problems in recognizing texts in certain circumstances, like texts are not big enough or fonts are not consistent. Therefore, this research suggests the way to recognize keywords in catalog with the Deep Learning algorithm which is state of the art in image-recognition area from 2010s. Single Shot Multibox Detector(SSD), which is a credited model for object-detection performance, can be used with structures re-designed to take into account the difference of text from object. But there is an issue that SSD model needs a lot of labeled-train data to be trained, because of the characteristic of deep learning algorithms, that it should be trained by supervised-learning. To collect data, we can try labelling location and classification information to texts in catalog manually. But if data are collected manually, many problems would come up. Some keywords would be missed because human can make mistakes while labelling train data. And it becomes too time-consuming to collect train data considering the scale of data needed or costly if a lot of workers are hired to shorten the time. Furthermore, if some specific keywords are needed to be trained, searching images that have the words would be difficult, as well. To solve the data issue, this research developed a program which create train data automatically. This program can make images which have various keywords and pictures like catalog and save location-information of keywords at the same time. With this program, not only data can be collected efficiently, but also the performance of SSD model becomes better. The SSD model recorded 81.99% of recognition rate with 20,000 data created by the program. Moreover, this research had an efficiency test of SSD model according to data differences to analyze what feature of data exert influence upon the performance of recognizing texts in images. As a result, it is figured out that the number of labeled keywords, the addition of overlapped keyword label, the existence of keywords that is not labeled, the spaces among keywords and the differences of background images are related to the performance of SSD model. This test can lead performance improvement of SSD model or other text-recognizing machine based on deep learning algorithm with high-quality data. SSD model which is re-designed to recognize texts in images and the program developed for creating train data are expected to contribute to improvement of searching system in E-commerce. Suppliers can put less time to register keywords for products and customers can search products with product-details which is written on the catalog.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.6
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• pp.595-603
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• 2015

With the advances of the big-data process technology, acquiring the real-time information from the massive image data taken by a mobile device inside a vehicle will be possible in the near future. Among the information that can be found around the vehicle, the route information is needed for safe driving. In this study, the automatic extraction of route information from the road sign imagery was dealt with. The scope of the route information in this study included the route number, route type, and their relationship with the driving direction. For the recognition of the route number, the modified Tesseract OCR (Optical Character Recognition) engine was used after extracting the rectangular-road-sign area with the Freeman chain code tracing algorithm. The route types (expressway, highway, rural highway, and municipal road) are recognized using the proposed algorithms, which are acquired from colour space analysis. Those road signs provide information about the route number as well as the roads that may be encountered along the way. In this study, such information was called “OTW (on the way)” or “TTW (to the way)” which between the two should be indicated is determined using direction information. Finally, the route number is matched with the direction information. Experiments are carried out with the road sign imagery taken inside a car. As a result, route numbers, route number type, OTW or TTW are successfully recognized, however some errors occurred in the process of matching TTW number with the direction.

• Journal of KIISE:Software and Applications
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• v.30 no.10
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• pp.904-911
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• 2003

In this paper, we propose an effective method for the distinction between machine-printed and handwritten Korean address images. It is important to know whether an input image is handwritten or machine-printed, because methods for handwritten image are quite different from those of machine-printed image in such applications as address reading, form processing, FAX routing, and so on. Our method consists of three blocks: valid connected components grouping, feature extraction, and classification. Features related to width and position of groups of valid connected components are used for the classification based on a neural network. The experiment done with live Korean address images has demonstrated the superiority of the proposed method. The correct classification rate for 3,147 testing images was about 98.85%.

• Toxicological Research
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• v.17
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• pp.227-236
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• 2001

The IARC Monographs Programme on the Evaluation of Carcinogenic Risks to Humans has reviewed, summarized and evaluated 869 environmental agents and exposures as oj June 2000. This large collection includes all relevant published epidemiological data on cancer in exposed humans and results of bioassays for carcinogenicity in experimental animals. Since 1986. cancer data have been systematically supplemented by summaries of other toxicological data that are relevant to assessments of carcinogenic hazard. These include summaries qf genetic and related effects of chemicals. which have been prepared as Genetic Activity Profiles (GAP) by the U.S. EPA in collaboration with IARC. As the Mono-graphs have proved increasingly valuable and influential worldwide. they have evolved into an encyclopedia on environmental carcinogenic risks to humans. However. the Monographs have historically been prepared only as printed books with limited distribution. and the Monographs Programme has needed to adjust to expectations oj wider availability. Since 1998 the evaluations and summaries have been globally accessible by Internet from IARC (http://www.iarc.fr) and the GAP profiles by Internet from EPA (http://www.epa.gov/gapdb/). with the two web sites linked. Improved EPN/ARC GAP database and software. GAP2000. now link GAP profiles directly to the appropriate IARC web pages for summaries of evaluations of a given compound and its overall IARC classification. During the year 2000. by means of optical character recognition (OCR) technology the entire series of IARC Monographs is being converted to an electronic version. The first edition is now available commercially in CD-ROM format and will soon become available on-line at .