• 한국콘텐츠학회논문지
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• 제11권12호
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• pp.511-521
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• 2011

영상 입출력 장치 사용이 증가함에 따라 컬러영상 내 문자영역 추출의 중요성 또한 높아지고 있다. 본 논문은 이러한 영상 내 문자영역을 효과적으로 추출하기 위해 레이블링 기법과 화소 단위의 밝기값 변화에 기반한 문자영역 추출 방법을 제안한다. 제안하는 방법은 레이블링 및 필터링 과정을 통해 비문자 영역을 미리 제거하고, 밝기값의 변화가 큰 문자영역의 특성을 이용하여 문자영역 후보군을 추출한 후 노이즈 제거 및 문자영역 병합의 후처리 과정을 통해 문자영역을 추출한다. 제안한 방법의 강점은 기존 방법보다 단순하면서도 높은 정확성에 있다. 실험 결과 제안한 방법의 정확도와 재현율, 비문자 추출의 역 비율(IRNTE)은 각각 99.59%, 98.65%, 82.30%로 측정되었다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권1호
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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.

• 스마트미디어저널
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• 제4권3호
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• pp.44-49
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• 2015

Text line segmentation is a preprocessing step in OCR, which can significantly influence the accuracy of document analysis applications. This paper proposes a novel methodology for the text line segmentation of handwritten documents. First, the average width of the connected components is used to form a 1-D Gaussian kernel and a smoothing operation is then applied to the input binary image. The adaptive binarization of the smoothed image forms the final text lines. In this work, the segmentation method involves two stages: firstly, the large connected components are labelled as a unique text line using text line area mapping. Secondly, the final refinement of the segmentation is performed using the Euclidean distance between the text line and small connected components. The group of uniquely labelled text candidates achieves promising segmentation results. The proposed approach works well on Korean and English language handwritten documents captured using a camera.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2005년도 추계종합학술대회
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• pp.979-982
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• 2005

This paper is to be pre-processing that decides the text recognizability and quality contained in natural image. Differentiated with the existing studies, In this paper, it suggests the application of partially unified color models, Coiflet Wavelet and text extraction algorithm that uses the closed curve edge features of LoG (laplacian of gaussian)operator. The text image included in natural image such as signboard has the same hue, saturation and value, and there is a certain thickness as for their feature. Each color element is restructured into closed area by LoG operator, the 2nd differential operator. The text area is contracted by Hough Transform, logical AND-OR operator of each color model and Minimum-Distance classifier. This paper targets natural image into which text area is added regardless of the size and resolution of the image, and it is confirmed to have more excellent performance than other algorithms with many restrictions.

• 디지털콘텐츠학회 논문지
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• 제17권4호
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• pp.219-226
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• 2016

일반 생활 속에서 우리 인간의 눈으로 정보를 인식하고 그 정보를 이용하는 것에는 한계가 없을 만큼 다양하고 방대하다. 그러나 인공지능이 발달한 현재의 기술로도, 인간의 시각 처리 능력에 비하면 턱없이 능력이 부족하다. 그럼에도 불구하고 많은 연구자들은 실생활 속에서 정보를 얻고자 하고 있고, 특히 글자로 된 정보를 인식하는데 많은 노력을 기울이고 있다. 글자를 인식하는 분야에서 일반적인 문서에서 글자를 추출하는 것은 일부 정보처리 분야에서 이용되고 있지만, 실영상에서 문자를 추출하고 인식하는 부분은 아직도 많이 부족하다. 그 이유는 실영상에서는 색깔, 크기, 방향, 공통점 등에서 다양한 특징을 갖고 있기 때문이다. 본 논문에서는 이런 다양한 환경에서 문자 영역을 추출하기 위하여 적응적 에지 강화 기반의 MSER을 적용하여 장면 텍스트 추출을 시도하고, 비교적 좋은 방법임을 실험으로 보인다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제11권3호
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• pp.115-124
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• 2022

본 논문에서는 최근 다양한 분야에서 많이 활용되고 있는 YOLO와 EAST 신경망을 이미지 속 문자열 탐지문제에 적용해보고 이들의 성능을 비교분석 해 보았다. YOLO 신경망은 일반적으로 이미지 속 문자영역 탐지에 낮은 성능을 보인다고 알려졌으나, 실험결과 YOLOv3는 문자열 탐지에 비교적 약점을 보이지만 최근 출시된 YOLOv4와 YOLOv5의 경우 다양한 형태의 이미지 속에 있는 한글과 영문 문자열 탐지에 뛰어난 성능을 보여줌을 확인하였다. 따라서, 이들 YOLO 신경망 기반 문자열 탐지방법이 향후 문자 인식 분야에서 많이 활용될 것으로 전망한다.

• ETRI Journal
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• 제33권1호
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• pp.78-88
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• 2011

In this paper, we present the Touch Text exTractor (Touch TT), an interactive text segmentation tool for the extraction of scene text from camera-based images. Touch TT provides a natural interface for a user to simply indicate the location of text regions with a simple touchline. Touch TT then automatically estimates the text color and roughly locates the text regions. By inferring text characteristics from the estimated text color and text region, Touch TT can extract text components. Touch TT can also handle partially drawn lines which cover only a small section of text area. The proposed system achieves reasonable accuracy for text extraction from moderately difficult examples from the ICDAR 2003 database and our own database.

• Journal of Computing Science and Engineering
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• 제5권3호
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• pp.161-166
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• 2011

We present a fast algorithm for Korean text extraction and segmentation from subway signboards using smart phone sensors in order to minimize computational time and memory usage. The algorithm can be used as preprocessing steps for optical character recognition (OCR): binarization, text location, and segmentation. An image of a signboard captured by smart phone camera while holding smart phone by an arbitrary angle is rotated by the detected angle, as if the image was taken by holding a smart phone horizontally. Binarization is only performed once on the subset of connected components instead of the whole image area, resulting in a large reduction in computational time. Text location is guided by user's marker-line placed over the region of interest in binarized image via smart phone touch screen. Then, text segmentation utilizes the data of connected components received in the binarization step, and cuts the string into individual images for designated characters. The resulting data could be used as OCR input, hence solving the most difficult part of OCR on text area included in natural scene images. The experimental results showed that the binarization algorithm of our method is 3.5 and 3.7 times faster than Niblack and Sauvola adaptive-thresholding algorithms, respectively. In addition, our method achieved better quality than other methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권9호
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• pp.2312-2325
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• 2013

In this paper, we propose a preprocessing method that it is to correct the distortion of text area in Korean signboard images as a preprocessing step to improve character recognition. Distorted perspective in recognizing of Korean signboard text may cause of the low recognition rate. The proposed method consists of four main steps and eight sub-steps: main step consists of potential vertical components detection, vertical components detection, text-boundary estimation and distortion correction. First, potential vertical line components detection consists of four steps, including edge detection for each connected component, pixel distance normalization in the edge, dominant-point detection in the edge and removal of horizontal components. Second, vertical line components detection is composed of removal of diagonal components and extraction of vertical line components. Third, the outline estimation step is composed of the left and right boundary line detection. Finally, distortion of the text image is corrected by bilinear transformation based on the estimated outline. We compared the changes in recognition rates of OCR before and after applying the proposed algorithm. The recognition rate of the distortion corrected signboard images is 29.63% and 21.9% higher at the character and the text unit than those of the original images.

• 한국가정과교육학회지
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• 제3권1호
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• pp.113-129
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• 1991

This study was conducted to compare the outward aspects, objectives, and the contents of Home Economics text books of middle schools of Korea, Japan, U.S.A. and England. The results were summarized as follows. 1. The outward aspects of tex books： The Korean text books were small in size and the quality of paper was inferior to those of foreign countries. The Japanese text books were written by many authors, contained many lab works and data. Text books of U.S.A. were big in size made with good quality paper and contained many colour pictures. Text books England contained many problems and lab works. 2. Objectives of the Home Economics and Unit objectives： The objective of the subjects of Home Economics was written only in Korean text books. The unit objectives were described most concretely and detailedly in Korean text books comparing with other countries. 3. Contents： Korean text books covered all six areas of foods, clothings, housing, home management, family and occupation and theoretical explanations prevailed. Japanese text books contained numerous lab works, lacked two areas of home management and occupation, thecontents included a few practical lab works two areas of home management and occupation, the contents included a few practical lab works. In the text books of U.S.A. contained all six areas of Home Economics were covered and special emphasis was placed on self discovory and self development, and vocational guidance was also stressed. The text book of England contained only three areas of Home Economics, clothing, foods and housing； the number of area was limited but the basic theories of covered area was intended to lead to self comprehension through questions and lab works.