• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.8
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• pp.1352-1357
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• 2002

This Paper describes algorithm to extract license plates in vehicle images. Conventional methods perform preprocessing on the entire vehicle image to produce the edge image and binarize it. Hough transform is applied to the binary image to find horizontal and vertical lines, and the license plate area is extracted using the characteristics of license plates. Problems with this approach are that real-time processing is not feasible due to long processing time and that the license plate area is not extracted when lighting is irregular such as at night or when the plate boundary does not show up in the image. This research uses the gray level transition characteristics of license plates to verify the digit area by examining the digit width and the level difference between the background area the digit area, and then extracts the plate area by testing the distance between the verified digits. This research solves the problem of failure in extracting the license plates due to degraded plate boundary as in the conventional methods and resolves the problem of the time requirement by processing the real time such that practical application is possible. This paper Presents a power automated license plate recognition system, which is able to read license numbers of cars, even under circumstances, which are far from ideal. In a real-life test, the percentage of rejected plates wan 13%, whereas 0.4% of the plates were misclassified. Suggestions for further improvements are given.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.37-44
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• 2006

In this paper, a new version of the ICM method(MICM, modified ICM) in which the contextual information is modelled by Markov random fields (MRF) is introduced. To extract the feature, a new local MRF model with a fitting block neighbourhood is proposed. This model selects contextual information not only from the relative intensity levels but also from the geometrically directional position of neighbouring cliques. Feature extraction depends on each block's contribution to the local variance. They discriminates it into several regions, for example context and background. Boundaries between these regions are also distinctive. The proposed algerian performs segmentation using directional block fitting procedure which confines merging to spatially adjacent elements and generates a partition such that pixels in unified cluster have a homogeneous intensity level. From experiment with ink rubbed copy images(Takbon, 拓本), this method is determined to be quite effective for feature identification. In particular, the new algorithm preserves the details of the images well without over- and under-smoothing problem occurring in general iterated conditional modes (ICM). And also, it may be noted that this method is applicable to the handwriting recognition.

• Journal of the Korean Data and Information Science Society
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• v.21 no.4
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• pp.689-697
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• 2010

In this study, we propose a new procedure for improving the character recognition of text area extracted from video images. The recognition of strings extracted from video, which are mixed with Hangul, English, numbers and special characters, etc., is more difficult than general character recognition because of various fonts and size, graphic forms of letters tilted image, disconnection, miscellaneous videos, tangency, characters of low definition, etc. We improved the recognition rate by taking commonly used letters and leaving out the barely used ones instead of recognizing all of the letters, and then using SVM and RDA character recognition methods. Our numerical results indicate that combining SVM and RDA performs better than other methods.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.12
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• pp.51-57
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• 1999

In this paper, an adaptive thresholding of the nonuniformly contrasted images is proposed through using the contrast pre-enhancement of the local regions and the bilinear interpolation between the local threshold values. The nonuniformly contrasted image is decomposed into 9${\times}$9 sized local regions, and the contrast is enhanced by intensifying the gray level difference of each low contrasted or blurred region. Optimal threshold values are obtained by iterative method from the gray level distribution of each contrast-enhanced local region. Discontinuities are reduced at the region of interest or at the characters by using bilinear interpolation between the neighboring threshold surfaces. Character recognition experiments are conducted using backpropagation neural network on the characters extracted from the nonuniformly contrasted document, PCB, and wafer images binarized through using the proposed thresholding and the conventional thresholding methods, and the results prove the relative effectiveness of the proposed scheme.

• The Journal of the Convergence on Culture Technology
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• v.4 no.4
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• pp.361-366
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• 2018

For each character constituting learning data, different fonts are added in pixel unit to create MASK, and then pixel values belonging to the MASK are divided into three groups. The experimental data are modified into skeletal forms, and then regional boundary operation is used to create a boundary that distinguishes the background region adjacent to the skeleton of the character from the background of the modified experimental data. Discordance values between the modified experimental data and the MASKs are calculated, and then the MASK with the minimum value is found. This MASK is selected as a finally recognized result for the given experiment data. The recognition algorithm using skeleton of the character and the regional boundary operation can easily extend the learning data set by adding new fonts to the given learning data, and also it is simple to implement, and high character recognition rate can be obtained.

• The Transactions of the Korea Information Processing Society
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• v.4 no.2
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• pp.465-482
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• 1997

In this paper, we have studied on cinstructing code-recognition shstem by neural network according to a image process taking the DOT classification code stamped on tire surface.It happened to a few problems that characters distorted in edge by diffused reflection and two adjacent characters take the same label,even very sen- sitive to illumination ofr recognition the stamped them on tire.Thus,this paper would propose the algorithm for tire code under being cinscious of these properties and prove the algorithm drrciency with a simulation.Also,we have suggerted the MCBP network composing of multi-linked recognizers of dffcient identify the DOT code being tire classification code.The MCBP network extracts the projection balue for classifying each character's rdgion after taking out the prjection of each chracter's region on X,Y axis,processes each chracters by taking 7$\times$8 normalization.We have improved error rate 3% through the MCBP network and post-process comparing the DOT code Database. This approach has a accomplished that learming time get's improvenent at 60% and recognition rate has become to 95% from 90% than BckPropagation with including post- processing it has attained greate rates of entire of tire recoggnition at 98%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.8A
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• pp.805-813
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• 2002

Document image should be classified into detailed regions as text, picture, table and etc through the geometric layout analysis if paper documents can be converted automatically into electronic documents. However, complexity of the document layout and variety of the size and density of a picture are the reason to make it difficult to analyze the geometric layout of the document images. In this paper, we propose the method which have a better performance of the region segmentation and classifications, and the line extraction in the table region than the commercial softwares and previous methods. The proposed method can segment the document into detailed regions by using connected components method even if its layout is complex. This method also classifies texts and pictures by using separable median filter even. Though their size and density are diverse, In addition, this method extracts the lines from the table adapting one dimensional median filter to the each horizontal and vertical direction, even though lines are deformed or texts attached to them.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.279-286
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• 2004

본 논문에서는 출입국자 관리의 효율성과 체계적인 출입국 관리를 위하여 위조 여권을 판별할 수 있는 지능형 여권 얼굴 인증 시스템을 제안한다. 제안하는 지능형 여권 얼굴 인증 시스템은 여권 이미지에서 여권 코드 문자열을 인식하여 여권 사용자의 사진 및 관련 정보를 여권 데이터베이스에서 추출한다. 추출된 출입국자의 사진 및 얼굴과 여권에 부착된 사진 및 얼굴과의 유사도 측정을 통하여 여권 사진의 위조 여부을 판단한다. 이때, 이미지의 유사도 측정을 위해서 다양한 실험을 통한 결과를 종합 분석해 본 결과 사진 영역의 인증에는 Luminance, Edge, RGB 특징이, 얼굴 영역의 인증을 위해서는 Hue, YIQ-I, YCbCr-Cb 특징이 효과적인 것으로 나타났으며 사진 영역의 유사도와 얼굴영역의 유사도가 모두 0.8이상인 경우 정상적인 여권으로 판정하고 그렇지 않은 경우 위조가 되었을 가능성이 있는 여권으로 판정하는 방법을 사용하여 FAR 3.1%, FRR 2.7%의 우수한 결과를 나타내었다.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1323-1331
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• 2017

Despite the fact that the copyright has grown into a large-scale business, there are many constant problems especially in image copyright. In this study, we propose an automatic object extraction and classification system for the scanned educational book image by combining document image processing and intelligent information technology like deep learning. First, the proposed technology removes noise component and then performs a visual attention assessment-based region separation. Then we carry out grouping operation based on extracted block areas and categorize each block as a picture or a character area. Finally, the caption area is extracted by searching around the classified picture area. As a result of the performance evaluation, it can be seen an average accuracy of 83% in the extraction of the image and caption area. For only image region detection, up-to 97% of accuracy is verified.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.15-18
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• 2000

In this paper, we propose a method for text extraction in the Web images. Our approach is based on contrast detecting and pixel component ratio analysis in mouse position. Extracted data with OCR can be used for real time dictionary call or language translation application in Web browser.