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

• Journal of Information Management
• /
• v.26 no.1
• /
• pp.16-51
• /
• 1995

Information management area faced new challenge arised from the developments of the computer and the information network, and the advent of information super highway. With deep perception of importance of the information, developments of information technologies, and change of the users' environment, we came to envision the digital library. This paper intends to describe the concept and function of the digital library, and to examine some of information technologies such as CD-ROM, OCR technology and image scanning, hypertext, hypermedia and multimedia. And it also considers the strategies for electronic information services and the applicability of the current information technology for digitalization by case studies of the existing database systems.

• International Journal of Computer Science & Network Security
• /
• v.22 no.7
• /
• pp.220-228
• /
• 2022

Image always carry useful information, detecting a text from scene images is imperative. The proposed work's purpose is to recognize scene text image, example boarding image kept on highways. Scene text detection on highways boarding's plays a vital role in road safety measures. At initial stage applying preprocessing techniques to the image is to sharpen and improve the features exist in the image. Likely, morphological operator were applied on images to remove the close gaps exists between objects. Here we proposed a two phase algorithm for extracting and recognizing text from scene images. In phase I text from scenery image is extracted by applying various image preprocessing techniques like blurring, erosion, tophat followed by applying thresholding, morphological gradient and by fixing kernel sizes, then canny edge detector is applied to detect the text contained in the scene images. In phase II text from scenery image recognized using MSER (Maximally Stable Extremal Region) and OCR; Proposed work aimed to detect the text contained in the scenery images from popular dataset repositories SVT, ICDAR 2003, MSRA-TD 500; these images were captured at various illumination and angles. Proposed algorithm produces higher accuracy in minimal execution time compared with state-of-the-art methodologies.

• Journal of Information Processing Systems
• /
• v.19 no.3
• /
• pp.289-301
• /
• 2023

Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.

• Journal of the Korea Convergence Society
• /
• v.9 no.9
• /
• pp.15-22
• /
• 2018

As smart devices become popular, users can use authentication services in various methods. Authentication services include authentication using an ID and a password, authentication using a sms, and authentication using an OTP(One Time Password). This paper proposed an authentication system that solves the security problem of knowledge-based authentication using optical character recognition and can easily and quickly authenticate users. The proposed authentication system extracts a character from an uploaded image by a user and authenticates the user using the extracted character information. The proposed authentication system has the advantage of not using a password or an OTP that are easily exposed or lost, and can not be authenticated without using accurate photographs. The proposed authentication system is platform independent and can be used for user authentication, file encryption and decryption.

• Korean Journal of Construction Engineering and Management
• /
• v.24 no.5
• /
• pp.35-43
• /
• 2023

Along with the increase in Multi-unit housing defect disputes, the importance of defect management is also increased. However, previous studies have mostly focused on the Multi-unit housing's 'common part'. In addition, there is a lack of research on the system for the 'management office', which is a part of the subject of defect management. These resulted in the lack of defect management capability of the management office and the deterioration of management quality. Therefore, this paper proposes a machine learning-based defect data management system for management offices. The goal is to solve the inconvenience of management by using Optical Character Recognition (OCR) and Natural Language Processing (NLP) modules. This system converts handwritten defect information into online text via OCR. By using the language model, the defect information is regenerated along with the form specified by the user. Eventually, the generated text is stored in a database and statistical analysis is performed. Through this chain of system, management office is expected to improve its defect management capabilities and support decision-making.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.41 no.2
• /
• pp.65-76
• /
• 2004

Today, most documents are electronically produced and their paleography is digitalized by imaging, resulting in a tremendous number of electronic documents in the shape of images. Therefore, to process these document images, many methods of document structure analysis and recognition have already been proposed, including font classification. Accordingly, the current paper proposes a font classification method for document images that uses non-negative matrix factorization (NMF), which is able to learn part-based representations of objects. In the proposed method, spatially total features of font images are automatically extracted using NMF, then the appropriateness of the features specifying each font is investigated. The proposed method is expected to improve the performance of optical character recognition (OCR), document indexing, and retrieval systems, when such systems adopt a font classifier as a preprocessor.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.9
• /
• pp.3701-3720
• /
• 2015

A system for the recognition of machine printed Arabic script is proposed. The Arabic script is shared by three languages i.e., Arabic, Urdu and Farsi. The three languages have a descent amount of vocabulary in common, thus compounding the problems for identification. Therefore, in an ideal scenario not only the script has to be differentiated from other scripts but also the language of the script has to be recognized. The recognition process involves the segregation of Arabic scripted documents from Latin, Han and other scripted documents using horizontal and vertical projection profiles, and the identification of the language. Identification mainly involves extracting connected components, which are subjected to Principle Component Analysis (PCA) transformation for extracting uncorrelated features. Later the traditional K-Nearest Neighbours (KNN) algorithm is used for recognition. Experiments were carried out by varying the number of principal components and connected components to be extracted per document to find a combination of both that would give the optimal accuracy. An accuracy of 100% is achieved for connected components >=18 and Principal components equals to 15. This proposed system would play a vital role in automatic archiving of multilingual documents and the selection of the appropriate Arabic script in multi lingual Optical Character Recognition (OCR) systems.

• International Journal of Contents
• /
• v.5 no.3
• /
• pp.50-56
• /
• 2009

Internet is being used for several activities by a great range of users. These activities include communication, e-commerce, education, and entertainment. Users are required to register regarding website in order to enroll web activities. However, registration can be done by automated hacking software. That software make false enrollments which occupy the resources of the website by reducing the performance and efficiency of servers, even stop the entire web service. It is crucial for the websites to have a system which has the capability of differing human users and computer programs in reading images of text. Completely Automated Public Turing Test to Tell Computers and Human Apart (CAPTCHA) is such a defense system against Optical Character Recognition (OCR) software. OCR can be defined as software which work for defeating CAPTCHA images and make countless number of registrations on the websites. This study proposes a new CAPTCHA context that is Korean CAPTCHA by means of the method which is splitting CAPTCHA images into several parts with random rotation values, and drawing random lines on a grid background by using Korean characters only. Lines are in the same color with the CAPTCHA text and they provide a distortion of image with grid background. Experimental results show that Korean CAPTCHA is a more secure and effective CAPTCHA type for Korean users rather than current CAPTCHA types due to the structure of Korean letters and the algorithm we are using: rotation and splitting. In this paper, the algorithm of our method is introduced in detail.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2023.11a
• /
• pp.990-991
• /
• 2023

커져만 가는 비건 시장에 비해서 비건 제품의 가격은 높고, 한정되어 있다. 성분표만을 보고 비건 여부를 파악하기에는 어렵고, 저시력자 및 시각장애인에게는 더욱 어려운 일이다. 치주 질환이나 당뇨를 포함한 크고 작은 다양한 질병으로 인해 육식 섭취 대신 불가피하게 채식을 실천해야 하는 경우 또는 가격 부담이 크고 찾기 어렵다. 그래서 비건 인증을 받은 제품 대신 일반 제품들 사이에서 비건에 적합한 제품을 찾는 데 도움이 되는 시스템을 개발하고자 한다. 본 논문에서는 저시력자 및 시각장애인을 위한 큰 글씨 화면, 음성 입출력 시스템 제공과 성분표 촬영을 통해 비건 적합 여부 및 알레르기 정보 제공, 사용자 특성 분석을 통한 UI 구성의 서비스를 제공한다. 성분표 촬영에 어려움을 겪는 저시력자 및 시각장애인에게 편리를 제공하기 위해 소프트웨어 뿐만 아니라 하드웨어를 구성한다.