• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.192-198
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• 2010

With the popularization of the mobile phone with a built-in camera, there are a lot of effort to provide useful information to users by detecting and recognizing the text in the video which is captured by the camera in mobile phone, and there is a need to detect the text regions in such mobile phone video. In this paper, we propose a method to detect the text regions in the mobile phone video. We employ morphological operation as a preprocessing and obtain binarized image using modified k-means clustering. After that, candidate text regions are obtained by applying connected component analysis and general text characteristic analysis. In addition, we increase the precision of the text detection by examining the frequency of the candidate regions. Experimental results show that the proposed method detects the text regions in the mobile phone video with high precision and recall.

• Journal of the Korea Society of Computer and Information
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• v.13 no.5
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• pp.77-84
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• 2008

This paper proposes a new approach to eliminate the reflectance component for the localization of text in natural scene images. Natural scene images normally have an illumination component as well as a reflectance component. It is well known that a reflectance component usually obstructs the task of detecting and recognizing objects like texts in the scene, since it blurs out an overall image. We have developed an approach that efficiently removes reflectance components while Preserving illumination components. We decided whether an input image hits Normal or Polarized for determining the light environment, using the histogram which consisted of a red component. In the normal image, we acquired the text region without additional processing. Otherwise we removed light reflecting from the object using homomorphic filtering in the polarized image. And then this decided the each text region based on the color merging technique and the Saliency Map. Finally, we localized text region on these two candidate regions.

• Journal of the Korea Convergence Society
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• v.10 no.5
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• pp.27-34
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• 2019

In this paper, we propose a method to remove the background area by analyzing the pattern of the character area. In the character detection result of the MSER(Maximally Stable External Regions) method which distinguishes a region having a constant contrast background regions were detected. To solve this problem, we use the MSER method in natural images, the background is removed by calculating the change rate by searching the character area and the background area which are not different from the areas where the contrast values are different from each other. However, in the background removed image, using the LBP(Local Binary Patterns) method, the area with uniform values in the image was determined to be a character area and character detection was performed. Experiments were carried out with simple images with backgrounds, images with frontal characters, and images with slanted images. The proposed method has a high detection rate of 1.73% compared with the conventional MSER and MSER + LBP method.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.15-22
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• 2019

In this paper, we proposed a Hangeul detection method using structural features of histogram, consonant, and vowel to solve the problem of Hangul which is separated and detected consonant and vowel The proposed method removes background by using DoG (Difference of Gaussian) to remove unnecessary noise in Hangul detection process. In the image with the background removed, we converted it to a binarized image using a cumulative histogram. Then, the horizontal position histogram was used to find the position of the character string, and character combination was performed using the vertical histogram in the found character image. However, words with a consonant vowel such as '가', '라' and '귀' are combined using a structural characteristic of characters because they are difficult to combine into one character. In this experiment, an image composed of alphabets with various backgrounds, an image composed of Korean characters, and an image mixed with alphabets and Hangul were tested. The detection rate of the proposed method is about 2% lower than that of the K-means and MSER character detection method, but it is about 5% higher than that of the character detection method including Hangul.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.61-67
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• 2021

In this paper, we propose a method of extracting and recognizing related information for research from images of the unstructured pulmonary function test papers using character detection and recognition techniques. Also, we develop a post-processing method to reduce the character recognition error rate. The proposed structuring method uses a character detection model for the pulmonary function test paper images to detect all characters in the test paper and passes the detected character image through the character recognition model to obtain a string. The obtained string is reviewed for validity using string matching and structuring is completed. We confirm that our proposed structuring system is a more efficient and stable method than the structuring method through manual work of professionals because our system's error rate is within about 1% and the processing speed per pulmonary function test paper is within 2 seconds.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.307-308
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• 2015

자동차 번호판의 문자를 검출하기 위한 과정 중 그림자가 있는 후면 번호판을 이진화하는 방법을 제안한다. 대부분의 경우 차량구조에 의한 그림자 발생이 문자를 검출하는데 오류를 발생시킨다. 이를 해결하기 위해 그림자 영역과 아닌 영역의 경계를 검출해야 한다. 하지만, 기존 방법은 히스토그램에서 세 개의 영역사이에 있는 임계값 2개를 수동으로 결정해야 되는 점과 현재번호판의 색상인 흰색 바탕에 검은 문자에 적용하면 문자 영역의 그림자 경계선 검출이 모호하다는 단점이 있다. 본 논문에서는 이 문제를 해결하기 위하여 슬라이딩 윈도우를 이용한 히스토그램과 탐색하는 픽셀의 좌, 우 픽셀들을 스캔하여 연결되지 않은 에지를 찾아 그림자 경계선 에지를 연결하는 방법을 제안한다.

• Annual Conference on Human and Language Technology
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• 1992.10a
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• pp.545-553
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• 1992

문자인식에 있어서 인식율을 높이기 위한 후처리의 한 방법으로서, 문법 정보를 이용하는 후처리를 제안하고자 한다. 즉, 문자 인식 시스템에 의해 인식된 국어문에 대해서 오인식된 문자를 포함하는 어절을 검출하고, 오인식된 문자의 적절한 후보를 선정하여 그에 따라 자동수정을 행하는 것을 전채 후처리 과정으로 전제한다. 본 논문에서는 형태소 분석을 통해 오인식된 부분을 검출하는 과정을 보임으로써 문자인식에 있어서 문법 정보를 이용하는 후처리의 가능성과 그 유효성을 보이는 것을 목적으로 한다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2009.01a
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• pp.181-184
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• 2009

본 논문은 번호판 내 명암도 대비를 이용한 자동차 번호판 검출 기법을 제안한다. 평균값 필터와 라플라시안 필터를 사용하여 영상의 잡음을 제거하는 동시에 에지 성분을 향상시킨 후 조명 환경 변화에 강인한 번호판 내 명암도 대비 특징을 이용하여 문자 후보를 검출한다. 다음으로 검출된 문자 후보가 열을 이루는 텍스트 후보를 검출하고, 이 영역을 Otsu 이진화 기업을 사용하여 x축에 투영하였을 시 나타나는 패턴을 평가함으로써 최종적으로 자동차 번호판을 검출하게 된다. 제안하는 기법의 성능을 평가하기 위해 다수의 데이터를 사용하여 실험하였고, 이를 분석하여 제안하는 기법의 우수성을 검증하였다.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.131-136
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• 2007

This paper proposes a new approach to eliminate the reflectance component for the detection of text in color images. Color images, printed by color printing technology, normally have an illumination component as well as a reflectance component. It is well known that a reflectance component usually obstructs the task of detecting and recognizing objects like texts in the scene, since it blurs out an overall image. We have developed an approach that efficiently removes reflectance components while preserving illumination components. We decided whether an input image hits Normal or Polarized for determining the light environment, using the histogram which consisted of a red component. We were able to go ahead through the ability to extract by reducing the blur phenomenon of text by light because reflection component by an illumination change and removed it and extracted text. The experimental results have shown a superior performance even when an image has a complex background. Text detection and recognition performance is influenced by changing the illumination condition. Our method is robust to the images with different illumination conditions.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.1
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• pp.139-148
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• 2015

In this paper, a new algorithm is proposed for detecting the product number of each thick plate and extracting each character of the product number from a image which contains several thick plates. In general, a image of thick plates contains several steal plates. To obtain the product number from the image, we first need to separate each plate. To do so, we use the line edges of thick plates and a clustering algorithm. After separating each plate, background parts are eliminated from the image of each plate. Background parts of an individual thick plate image consist of the dark part of steel and the white part of paint which is used for printing the product number. We propose a two-tiered method where dark background parts are first eliminated and then white parts are eliminated. Finally, each character is extracted from the product number image using the characteristics of product number. The results of the experiments on the various steal plates images emphasize that the proposed algorithm detects each thick plate and extracts the product number from a image effectively.