• The Journal of the Convergence on Culture Technology
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• v.4 no.3
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• pp.241-246
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• 2018

Because of the mechanical problems of input camera equipment, image restoration process is performed in order to minimize recognition errors due to the noise problem generated in test data image. The image restoration method resolves the noise problem by examining the numbers and positions of the Direct neighbors and the Indirect neighbors for each pixel constituting the test data. As a result, satisfactory recognition result can be obtained by eliminating the noise problem generated in the test data through the image restoration process as much as possible and also by calculating the differences between the learning data and the test data in the area unit, thereby reducing the possibility of recognition error by the noise problem.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.1009-1015
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• 2009

Handwriting verification is a technique of distinguishing the same person's handwriting specimen from imitations with any two or more texts using one's handwriting individuality. This paper suggests an effective verification method for the handwritten signature or text on the off-line environment using pattern recognition technology. The core processes of the method which has been researched in this paper are extraction of letter area, extraction of features employing structural characteristics of handwritten text, feature analysis employing DTW(Dynamic Time Warping) algorithm and PCA(Principal Component Analysis). The experimental results show a superior performance of the suggested method.

• Journal of the Korea Convergence Society
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• v.10 no.1
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• pp.103-113
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• 2019

License plate recognition camera is dedicated device designed for acquiring images of the target vehicle for recognizing letters and numbers in a license plate. Mostly, it is used as a part of the system combined with server and image analysis module rather than as a single use. However, building a system for vehicle license plate recognition is costly because it is required to construct a facility with a server providing the management and analysis of the captured images and an image analysis module providing the extraction of numbers and characters and recognition of the vehicle's plate. In this study, we would like to develop an embedded type convergent camera (Edge Base) which can expand the function of the camera to not only the license plate recognition but also the security CCTV function together and to perform two functions within the camera. This embedded type convergence camera equipped with a high resolution 4K IP camera for clear image acquisition and fast data transmission extracted license plate area by applying YOLO, a deep learning software for multi object recognition based on open source neural network algorithm and detected number and characters of the plate and verified the detection accuracy and recognition accuracy and confirmed that this camera can perform CCTV security function and vehicle number plate recognition function successfully.

• The KIPS Transactions:PartB
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• v.13B no.4 s.107
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• pp.479-488
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• 2006

The iconic language interlace is designed to provide more convenient environments for communication to the target system than the keyboard-based interface. For this work, tendencies and features of vocabulary are analyzed in conversation corpora constructed from the corresponding domains with high degree of utilization, and the meaning and vocabulary system of iconic language are constructed through application of natural language processing methodologies such as morphological, syntactic and semantic analyses. The part of speech and grammatical rules of iconic language are defined in order to make the situation corresponding the icon to the vocabulary and meaning of the Korean language and to communicate through icon sequence. For linguistic ambiguity resolution which may occur in the iconic language and for effective semantic processing, semantic data focused on situation of the iconic language are constructed from the general purpose Korean semantic dictionary and subcategorization dictionary. Based on them, the Korean language generation from the iconic interface in semantic domain is suggested.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.125-137
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• 2011

Recently the research on the system using images taken from camera phones as input is actively conducted. This paper proposed a system that shows wine pictures which are similar to the input wine label in order. For the calculation of the similarity of images, the representative color of each cell of the image, the recognized text color, background color and distribution of feature points are used as the features. In order to calculate the difference of the colors, RGB is converted into CIE-Lab and the feature points are extracted by using Harris Corner Detection Algorithm. The weights of representative color of each cell of image, text color and background color are applied. The image similarity is calculated by normalizing the difference of color similarity and distribution of feature points. After calculating the similarity between the input image and the images in the database, the images in Database are shown in the descent order of the similarity so that the effort of users to search for similar wine labels again from the searched result is reduced.

• 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.

• The Transactions of the Korea Information Processing Society
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• v.4 no.3
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• pp.859-870
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• 1997

Computerized map reading system is one of the most important application areas in the image processing.A map databaes can be used for a wide range of scial activities such as narural resource assessment,regional plan-ming,and reaffic nabigation system. The road segments,however,are extracted as briken in the area where they are overlapped and interupted by chracters and symbols.Few approaches have been taken to complete road segnents interupted by map symbols.In this paper,a movel approach for the extracation and completion of road segements interupted by map symbols is proposed using neural networks.The system is applied to 1/25,000 scaled maps published by the Grographical Survey Unstitute of Ministry of Construction of Korea.It will be shown that the system can extract and reconstruct road segmetns for the various areas of maps sucessfully.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.45 no.6
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• pp.70-77
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• 2008

This paper proposes a new text candidate region detection method that uses genetic algorithm based on information of the segmented regions. In image segmentation, a classification of the pixels at each color channel and a reclassification of the region-unit for reducing inhomogeneous clusters are performed. EWFCM(Entropy-based Weighted C-Means) algorithm to classify the pixels at each color channel is an improved FCM algorithm added with spatial information, and therefore it removes the meaningless regions like noise. A region-based reclassification based on a similarity between each segmented region of the most inhomogeneous cluster and the other clusters reduces the inhomogeneous clusters more efficiently than pixel- and cluster-based reclassifications. And detecting text candidate regions is performed by genetic algorithm based on energy and variance of the directional edge components, the number, and a size of the segmented regions. The region information-based detection method can singles out semantic text candidate regions more accurately than pixel-based detection method and the detection results will be more useful in recognizing the text regions hereafter. Experiments showed the results of the segmentation and the detection. And it confirmed that the proposed method was superior to the existing methods.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.253-260
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• 2009

Many algorithms for computer vision and pattern recognition have recently been implemented on GPU (graphic processing unit) for faster computational times. However, the implementation has two problems. First, the programmer should master the fundamentals of the graphics shading languages that require the prior knowledge on computer graphics. Second, in a job that needs much cooperation between CPU and GPU, which is usual in image processing and pattern recognition contrary to the graphic area, CPU should generate raw feature data for GPU processing as much as possible to effectively utilize GPU performance. This paper proposes more quick and efficient implementation of neural networks on both GPU and multi-core CPU. We use CUDA (compute unified device architecture) that can be easily programmed due to its simple C language-like style instead of GPU to solve the first problem. Moreover, OpenMP (Open Multi-Processing) is used to concurrently process multiple data with single instruction on multi-core CPU, which results in effectively utilizing the memories of GPU. In the experiments, we implemented neural networks-based text extraction system using the proposed architecture, and the computational times showed about 15 times faster than implementation on only GPU without OpenMP.

• Journal of KIISE:Software and Applications
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• v.26 no.11
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• pp.1342-1349
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• 1999

본 논문에서는 문서 내의 인접한 두 행 사이에는 일정한 두께의 공백 행이 존재하며 그 공백 행의 기울기는 실제 문서의 기울어진 정도를 반영한다는 사실에 기반하여, 선형적으로 기울어진 문서 영상의 기울기 추정을 위한 고속의 알고리즘을 제안한다. 먼저, 간단한 모폴로지 연산(dilation)을 이용하여 문자행 영역과 공백행 영역을 분리한 후, 이를 일정 간격으로 수직 샘플링하여 수직선 상에 있는 모든 공백행의 중심점(행간점)을 찾는다. 동일한 공백 행 상에 있는 인접한 두 행간점 간에 기울기를 계산하고, 전체 영상으로부터 이들의 분포를 조사하여 최대 빈도를 가지는 기울기를 입력 문서의 기울기로 추정한다. 실험에서는 제안한 알고리즘을 필기체 및 인쇄체를 포함하는 다양한 형태의 가로쓰기 문서에 적용한 결과를 보인다.Abstract In this paper, we propose a fast algorithm to estimate the skew angle of linearly skewed document images. This paper is based on the fact that there is a blank line with uniform thickness between two adjacent text lines and the slope of the line is the same as that of the document. Firstly, we apply a dilation operation to the image to separate blank lines from text lines, and we detect center points of blank lines along the vertically sampled lines. Then we calculate the slope between neighboring center points in the same blank line. Calculated slopes for the entire image are accumulated on the histogram to display the distribution of them. Finally, the peak in the histogram is detected and estimated as the slope of the document image. In the experiments, we adopted a lot of images of various format with hand-printed or machine-printed document to verify our algorithm.