• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.345-359
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• 2017

Optical Character Recognition (OCR) that has been a main research topic of computer vision and artificial intelligence now extend its applications to detection of text area from video or image contents taken by camera devices and retrieval of text information from the area. This paper aims to implement a binarization algorithm that removes user intervention and provides robust performance to outdoor lights by using TopHat algorithm and channel transformation technique. In this study, we particularly concentrate on text information of outdoor signboards and validate our proposed technique using those data.

• Electronics and Telecommunications Trends
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• v.4 no.2
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• pp.124-142
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• 1989

인간의 정보교환이나 기록의 매체로써 인간사회에서 중요한 문자는 시간이 경과함에따라 사용량이 비약적으로 증가해 왔으며, 앞으로도 증가 추세는 더욱 가증될 전망이다. 이러한 계속적인 정보의 증가는 활자를 매체로 하는 수용 한계를 넘어, 이미 computer를 활용하지 않으면 안되게 되었다. 특히, 기존의 문서화 되어있는 필요한 많은 data와 나날이 늘어나는 정보의 양을 database화 하여, 원하는 정보를 신속히 찾아내기 위해서는 기존의 key board를 이용하여 사람의 손으로 입력하는 방법보다 신속하고 정확한 입력장치개발이 요구된다. 이러한 data입력장치의 개발이 선행되지 않는다면 computer의 정보처리 속도와 연산속도가 아무리 향상되어도 효율적인 정보처리를 이룩할 수 없을것이다. 그러므로 이러한 것을 실현하기 위해서는 먼저 문자의 인식이 필요불가결하다. 본 논문에서는 문자인식의 현황과 문제점을 제시함으로써 효율적이고 경제적인 문자인식 sysyem 구축에 도움이 되기 바라며 이하 OCR(Optical Character Recognition)의 역사와 발전, 문자인식 방법과 문자인식 system의 구성, On-line 문자인식과 Off-line 문자인식에 관하여 논하고 결론을 맺는다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.239-240
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• 2013

The quality of document images captured by digital camera might be degraded because of non-uniform illumination condition. The high illumination (glare distortion) affects on the contrast condition of the document images. This condition leads to the poor contrast condition of the text in document image. So, optical character recognition (OCR) system might hardly recognize text in the high illuminated area. The method to increase the contrast condition between text (foreground) and background in high illuminated area is proposed in this paper.

• Smart Media Journal
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• v.2 no.1
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• pp.31-36
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• 2013

A robust and efficient text extraction is very important for an accuracy of Optical Character Recognition (OCR) systems. Natural scene images with degradations such as uneven illumination, perspective distortion, complex background and multi color text give many challenges to computer vision task, especially in text extraction. In this paper, we propose a method for extraction of the text in signboard images based on a combination of mean shift algorithm and weighting scheme of hue and saturation in HSI color space for clustering algorithm. The number of clusters is determined automatically by mean shift-based density estimation, in which local clusters are estimated by repeatedly searching for higher density points in feature vector space. Weighting scheme of hue and saturation is used for formulation a new distance measure in cylindrical coordinate for text extraction. The obtained experimental results through various natural scene images are presented to demonstrate the effectiveness of our approach.

• Journal of Information Processing Systems
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• v.16 no.6
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• pp.1437-1446
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• 2020

For the last few years, smart devices have begun to occupy an essential place in the life of children, by allowing them to access a variety of language activities and books. Various studies are being conducted on using smart devices for education. Our study extracts images and texts from kids' book with smart devices and matches the extracted images and texts to create new images that are not represented in these books. The proposed system will enable the use of smart devices as educational media for children. A deep convolutional generative adversarial network (DCGAN) is used for generating a new image. Three steps are involved in training DCGAN. Firstly, images with 11 titles and 1,164 images on ImageNet are learned. Secondly, Tesseract, an optical character recognition engine, is used to extract images and text from kids' book and classify the text using a morpheme analyzer. Thirdly, the classified word class is matched with the latent vector of the image. The learned DCGAN creates an image associated with the text.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.07a
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• pp.323-324
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• 2020

본 논문에서는 저사양 컴퓨터를 클러스터링하여 서버의 성능 향상에 관해 서술한다. 이 시스템은 다수의 컴퓨터를 하나의 클러스터로 연결하고 쿠버네티스를 활용하여 노드 간의 부하를 관리하여 컴퓨팅 시스템의 처리량을 최대화하고, 각 작업의 수행시간을 최소화한다. 또한, 이 시스템은 수행 중인 작업의 메모리 요구량과 각 노드의 부하 상태를 파악하여 작업을 동적으로 할당한다. 본 논문에서는 광학식 문자 판독(OCR)을 동작시키며 단독 서버 상태와 노드를 연결한 클러스터링 시스템과의 처리 속도를 비교하여 저사양 컴퓨터로도 효과적인 성능의 서버 구축을 할 수 있음을 보인다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2021.06a
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• pp.219-222
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• 2021

본 논문에서는 시각-관성 측위시스템(Visual-Inertial Odometry, VIO)에서 광학 문자 인식(Optical Character Recognition, OCR)을 활용해 문자의 영역을 찾아내고, 그 위치를 기억해 측위시스템에서 다시 인식되었을 때 비교하기 위해 위치와 특징점을 저장하고자 한다. 먼저, 실시간으로 움직이는 카메라의 영상에서 문자를 찾아내고, 카메라의 상대적인 위치를 이용하여 문자가 인식된 위치와 특징점을 저장하는 방법을 제안한다. 또한 저장된 문자가 다시 탐색되었을 때, 문자가 재인식되었는 지 판별하기 위한 방법을 제안한다. 인공적인 마커나 미리 학습된 객체를 사용하지 않고 상황에 따른 문자를 사용하는 이 방법은 문자가 존재하는 범용적인 공간에서 사용이 가능하다.

• The Journal of Bigdata
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• v.5 no.1
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• pp.29-39
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• 2020

Purpose The atmosphere of respect for individual tastes that have spread throughout society has changed the consumption trend. As a result, the travel industry is also seeing customized travel as a new trend that reflects consumers' personal tastes. In particular, there is a growing interest in 'film-induced tourism', one of the areas of travel industry. We hope to satisfy the individual's motivation for traveling while watching movies with customized travel proposals, which we expect to be a catalyst for the continued development of the 'film-induced tourism industry'. Design/methodology/approach In this study, we implemented a methodology through 'OCR' of extracting and suggesting film location information that viewers want to visit. First, we extract a scene from a movie selected by a user by using 'OpenCV', a real-time image processing library. In addition, we detected the location of characters in the scene image by using 'EAST model', a deep learning-based text area detection model. The detected images are preprocessed by using 'OpenCV built-in function' to increase recognition accuracy. Finally, after converting characters in images into recognizable text using 'Tesseract', an optical character recognition engine, the 'Google Map API' returns actual location information. Significance This research is significant in that it provides personalized tourism content using fourth industrial technology, in addition to existing film tourism. This could be used in the development of film-induced tourism packages with travel agencies in the future. It also implies the possibility of being used for inflow from abroad as well as to abroad.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.967-973
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• 2020

As the implementation of export controls is spreading, the importance of classifying strategic items is increasing, but Korean export companies that are new to export controls are not able to understand the concept of strategic items, and it is difficult to classifying strategic items due to various criteria for controlling strategic items. In this paper, we propose a method that can easily approach the process of classification by lowering the barrier to entry for users who are new to export controls or users who are using classification of strategic items. If the user can confirm the decision result by providing a manual or a catalog for the procedure of classifying strategic items, it will be more convenient and easy to approach the method and procedure for classfying strategic items. In order to achieve the purpose of this study, it utilizes deep learning, which are being studied in image recognition and classification, and OCR(optical character reader) technology. And through the research and development of the support tool, we provide information that is helpful for the classification of strategic items to our companies.

• Journal of Korea Multimedia Society
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• v.19 no.11
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• pp.1837-1851
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• 2016

Newspaper reader mobile applications using text-to-speech (TTS) function enable blind people to read newspaper contents. But, tables cannot be easily read by the reader program because most of the tables are stored as images in the contents. Even though we try to use OCR (Optical character reader) programs to recognize letters from the table images, it cannot be simply applied to the table reading function because the table structure is unknown to the readers. Therefore, identification of exact location of each table cell that contains the text of the table is required beforehand. In this paper, we propose an efficient image processing algorithm to recognize all the cells in tables by identifying columns and rows in table images. From the cell location data provided by the table column and row identification algorithm, we can generate table structure information and table reading scenarios. Our experimental results with table images found commonly in newspapers show that our cell identification approach has 100% accuracy for simple black and white table images and about 99.7% accuracy for colored and complicated tables.