• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.220-221
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• 2022

선박에선 의료관리자가 선박 의약품의 처방 및 관리를 하고 있으며 이는 대부분 항해사로 지정된다. 항해사의 고유 업무와 전문의료지식 부족으로 의약품 관리가 체계적으로 이루어지지 않고 수기로 기록되는 문제점이 있다. 본 연구에서는 영상인식기반의 선박 의약품 종합 관리 시스템을 개발하여 의약품 관리를 자동화하고 의료관리자의 업무 효율성을 증가시키고자 한다. 시스템은 의약품 용기·포장지를 촬영한 영상으로부터 글자를 인식하는 OCR(Optical Character Recognition) 기술을 활용한 모듈, 바코드를 인식모듈, 사용자가 검색할 수 있는 모듈로 구성되어있으며 선박 의약품을 데이터베이스화하여 전산으로 관리할 수 있다. 또한 시스템을 통하여 의약품 재고 관리를 하거나 의약품의 사용법을 확인할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.484-485
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• 2024

광학 문자 인식으로 영수증을 인식하여 텍스트를 추출하는 방식으로 사용자에게 가계부 작성에서의 편리함을 제공하고자 한다. 이를 제작하는 데 필요한 광학 문자 인식 API 활용과 서버, 클라이언트와 같은 가계부 애플리케이션에 대한 개발 요소를 소개한다.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.699-706
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• 2021

Existing license plate recognition system is used as an optical character recognition method, but a method of using deep learning has been proposed in recent studies because it has problems with image quality and Korean misrecognition. This requires a lot of data collection, but the collection of license plates is not easy to collect due to the problem of the Personal Information Protection Act, and labeling work to designate the location of individual license plates is required, but it also requires a lot of time. Therefore, in this paper, to solve this problem, five types of license plates were created using a virtual Korean license plate generation program according to the notice of the Ministry of Land, Infrastructure and Transport. And the generated license plate is synthesized in the license plate part of collectable vehicle images to construct 10,147 learning data to be used in deep learning. The learning data classifies license plates, Korean, and numbers into individual classes and learn using YOLOv5. Since the proposed method recognizes letters and numbers individually, if the font does not change, it can be recognized even if the license plate standard changes or the number of characters increases. As a result of the experiment, an accuracy of 96.82% was obtained, and it can be applied not only to the learned license plate but also to new types of license plates such as new license plates and eco-friendly license plates.

• Journal of Intelligence and Information Systems
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• v.10 no.2
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• pp.65-77
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• 2004

This paper presents a priori and the local font classification method. The font classification uses ascenders, descenders, and serifs extracted from a word image. The gradient features of those sub-images are extracted, and used as an input to a neural network classifier to produce font classification results. The font classification determines 2 font styles (upright or slant), 3 font groups (serif sans-serif or typewriter), and 7-font names (Postscript fonts such as Avant Garde, Helvetica, Bookman, New Century Schoolbook, Palatine, Times, and Courier). The proposed a priori and local font classification method allows an OCR system consisting of various font-specific character segmentation tools and various mono-font character recognizers. Experiments have shown font classification accuracies reach high performance levels of about 95.4 percent even with severely touching characters. The technique developed for tile selected 7 fonts in this paper can be applied to any other fonts.

• Journal of Korean Society of Archives and Records Management
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• v.22 no.4
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• pp.129-149
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• 2022

Records have temporal characteristics, including the past and present; linguistic characteristics not limited to a specific language; and various types categorized in a complex way. Processing records such as text, video, and audio in the life cycle of records' creation, preservation, and utilization entails exhaustive effort and cost. Primary natural language processing (NLP) technologies, such as machine translation, document summarization, named-entity recognition, and image recognition, can be widely applied to electronic records and analog digitization. In particular, Korean deep learning-based NLP technologies effectively recognize various record types and generate record management metadata. This paper provides an overview of Korean NLP technologies and discusses considerations for applying NLP technology in records management. The process of using NLP technologies, such as machine translation and optical character recognition for digital conversion of records, is introduced as an example implemented in the Python environment. In contrast, a plan to improve environmental factors and record digitization guidelines for applying NLP technology in the records management field is proposed for utilizing NLP technology.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.320-322
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• 2021

As the world's elderly population increases, the proportion of visually impaired is also increasing, and there are still many restrictions on the use of outside activities, such as safety problems and lack of guidance information. To solve this problem, research on smart devices such as smart glasses with optical character recognition (OCR) function is being actively conducted. In this paper, we propose a system that recognizes obstacles ahead and informs information by voice, and also guides the way to the destination. Using the deep learning object recognition model Yolo, it let them to recognize the risk factors as obstacles such as stairs and Larva cones. and it also deliver the information with a voice. so you can expect that the visually impaired can do a lot of different activity even more now that system takes the visually impaired to the destination by using the directions API, voice recognition, TTS library.

• Database Research
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• v.34 no.3
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• pp.148-158
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• 2018

The optical character recognition (OCR) is a technique to extract and recognize texts from images. It is an important preprocessing step in data analysis since most actual text information is embedded in images. Many OCR engines have high recognition accuracy for images where texts are clearly separable from background, such as white background and black lettering. However, they have low recognition accuracy for images where texts are not easily separable from complex background. To improve this low accuracy problem with complex images, it is necessary to transform the input image to make texts more noticeable. In this paper, we propose a method to segment an input image into text lines to enable OCR engines to recognize each line more efficiently, and to determine the final output by comparing the recognition rates of CLAHE module and Two-step module which distinguish texts from background regions based on image processing techniques. Through thorough experiments comparing with well-known OCR engines, Tesseract and Abbyy, we show that our proposed method have the best recognition accuracy with complex background images.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.310-312
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• 2016

OCR(Optical Character Recognition)의 오류를 줄이기 위해 본 논문에서는 교정 어휘 쌍의 혼동 행렬(confusion matrix)과 나이브 베이즈 분류기($na{\ddot{i}}ve$ Bayes classifier)를 이용한 철자 교정 시스템을 제안한다. 본 시스템에서는 철자 오류 중 한글에 대한 철자 오류만을 교정하였다. 실험에 사용된 말뭉치는 한국어 원시 말뭉치와 OCR 출력 말뭉치, OCR 정답 말뭉치이다. 한국어 원시 말뭉치로부터 자소 단위의 언어모델(language model)과 교정 후보 검색을 위한 접두사 말뭉치를 구축했고, OCR 출력 말뭉치와 OCR 정답 말뭉치로부터 교정 어휘 쌍을 추출하고, 자소 단위로 분해하여 혼동 행렬을 만들고, 이를 이용하여 오류 모델(error model)을 구축했다. 접두사 말뭉치를 이용해서 교정 후보를 찾고 나이브 베이즈 분류기를 통해 확률이 높은 교정 후보 n개를 제시하였다. 후보 n개 내에 정답 어절이 있다면 교정을 성공하였다고 판단했고, 그 결과 약 97.73%의 인식률을 가지는 OCR에서, 3개의 교정 후보를 제시하였을 때, 약 0.28% 향상된 98.01%의 인식률을 보였다. 이는 한글에 대한 오류를 교정했을 때이며, 향후 특수 문자와 숫자 등을 복합적으로 처리하여 교정을 시도한다면 더 나은 결과를 보여줄 것이라 기대한다.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06c
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• pp.412-416
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• 2007

본 논문에서는 OCR(Optical Character Recognition)의 정확도를 위해 인쇄체 한글 문서 영상에 대한 자동 평가방법을 제안한다. 자동 평가방법은 문서가 스캔된 상태에 따라 낮은 해상도, 영상 자체의 기울어짐, 많은 잡음 등을 판단하여 인식하지 않고도 인식률을 추측할 수 있다. 평가방법은 영상 자체의 밝기, 기울기, 영역의 특징, 문자의 상태 등을 특징 항목으로 만들어 점수를 산출한다. 각 항목의 점수는 가장 높은 인식률을 가지는 영상의 특징 값을 기준으로 삼는다. 각각의 특징에 대해 점수가 산출되면 인식률에 높은 비중을 차지하는 특징에 높은 가중치를 적용하여 최종 점수를 산출한다. 영상 평가방법을 통해 높은 점수를 얻은 영상은 상용 인식기를 통해 인식한 결과 높은 인식률을 나타냈고, 평가방법에서 낮은 점수를 받은 영상은 상대적으로 낮은 인식률을 나타냈다. 본 논문에서 제안하는 문서영상을 위한 자동 평가방법은 인식기를 사용하지 않고 영상의 품질을 측정하기 때문에 빠른 시간에 인식률을 추측할 수 있고, 낮은 인식률을 보일 수 있는 영상에 대해서는 항목별 점수를 피드백으로 사용할 수 있어 인식하기전 문서 영상의 전처리에 과정에 도움을 줄 수 있다.

• 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.