• The KIPS Transactions:PartD
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• v.17D no.1
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• pp.59-66
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• 2010

Traditional OCR engines are designed to the scanned documents in calibrated environment. Three dimensional perspective distortion and smooth distortion in images are critical problems caused by un-calibrated devices, e.g. image from smart phones. To meet the growing demand of character recognition of texts embedded in the photos acquired from the non-calibrated hand-held devices, we address the problem in three categorical aspects: rotational invariant method of text region extraction, scale invariant method of text line segmentation, and three dimensional perspective mapping. With the integration of the methods, we developed an OCR for camera-captured images.

• Proceedings of the Korean Information Science Society Conference
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• 2001.10b
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• pp.418-420
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• 2001

본 논문에서는 뉴스 비디오의 정지 영상에서 뉴스 자막과 배경 문자를 추출하기 위한 새로운 방법을 제안한다. 본 논문에서는 일차적으로 입력 컬러 영상을 그레이 영상으로 변환한 후 입력 영상의 명암 대비를 강화시키기 위해 명암 대비 스트레칭을 적용한다. 이후 명암 대비 스트레칭된 영상의 분할을 위해 적응적 임계값을 적용하고 다음 단계에서 문자와 유사한 영역들을 적당한 크기 의 structuring element를 이용하여 제거하는 1차 하부 단계와 모폴로지 녹임(erosion)을 적용한 영상과 모폴로지(열림닫힘[OpenClose]+닫힘열림[CloseOpen])/2가 적용된 영상 사이의 차이 영상을 구하는 2차 하부 단계를 적용시킨다. 마지막 단계에서 각 후보 영역들 중 실제 자막 영역을 추출해내기 위해, 후보 문자 영역의 화소수 비율과 외곽선의 화소수의 비율, 그리고 장축과 단축간의 비율 등에 대해 필터링을 적용한다. 본 논문에서는 임의의 300개의 뉴스영상을 입력 값으로 실험한 결과 93.6%의 우수한 인식률을 얻을 수 있었다. 또한 본 논문에서 제안한 방법은 structuring element의 크기 조절을 통해 크기가 다른 다양한 이미지에서도 좋은 성능을 거둘 수 있다.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.444-450
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• 1999

This paper deals with several methods: the clustering method that uses k-means algorithm to abstract the area of characters on the image document and the distance function that suits for the HIS coordinate system to cluster the image. For the prepossessing step to recognize this, or the method of characters segmentate, the algorithm to abstract a discrete character is also proposed, using the linking picture element. This algorithm provides the feature that separates any character such as the touching or overlapped character. The methods of projecting and tracking the edge have so far been used to segment them. However, with the new method proposed here, the picture element extracts a discrete character with only one-time projection after abstracting the character string. it is possible to pull out it. dividing the area into the character and the rest (non-character). This has great significance in terms of processing color documents, not the simple binary image, and already received verification that it is more advanced than the previous document processing system.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.2
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• pp.14-20
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• 2003

This paper presents a text extraction method of camera based document image. It is more difficult to recognize camera based document image in comparison with scanner based image because of segmentation problem due to variable lighting condition and versatile fonts. Both document binarization and character extraction are important processes to recognize camera based document image. After converting color image into grey level image, gray level normalization is used to extract character region independent of lighting condition and background image. Local adaptive binarization method is then used to extract character from the background after the removal of noise. In this character extraction step, the information of the horizontal and vertical projection and the connected components is used to extract character line, word region and character region. To evaluate the proposed method, we have experimented with documents mixed Hangul, English, symbols and digits of the ETRI database. An encouraging binarization and character extraction results have been obtained.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.3
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• pp.608-618
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• 2007

Image binarization is an important preprocessing to identify the object of interest by dividing pixels into the background and object. We proposes efficient binarization method and a stroke reconstruction method of the low quality character image for an effective character recognition. First, the character image is binarized by using the both advantages of local and global thresholding method and then the noise elimination around the character stroke and the hole filling on the stoke by the analysis of the binarized stroke image are performed to enhance the quality of the character stroke. Proposed binarization algorithm for character image achieved an efficiency of both processing speed and performance by the adaptive threshold selection. Moreover, We could get a high qualify binary image by a stroke reconstruction of the step-by-step denoising process.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.97-100
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• 2013

In this paper, we propose a detection method of the character rather than cursive, containing many components of the vertical and horizontal direction in complex background image. The characters have many dense corners but the background has few sparse corners. So we use harris corner detector and cluster the corners by using the position of the detected corners for detecting character regions. To merge or filter character regions, we analysis a histogram of gray image of character regions. In each improved region, we compare histograms of R, G, B channels to detect characters.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2006.05a
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• pp.689-692
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• 2006

본 논문에서는 그림 영역에서 단어 영상을 효과적으로 추출하는 방법을 제안한다. 제안 방법은 문자 성분과 그래픽 성분을 분류하기 위하여 구성 원소들의 통계값을 이용하는 상자그림 분석을 응용하고, 분류된 문자 성분들에 대하여 지역적 밀집도를 분석하여 문자 영역을 추출한다. 추출된 문자 영역에서 문자열 및 단어 영상을 추출하는 방법은 투영 히스토그램 분석 등을 적용한다. 제안 방법은 임계치 대신에 그림 영역의 통계값을 이용하였기 때문에 그림의 형태 변화에 민감하지 않으며, 지역적 밀집도 분석으로 보다 정확한 문자 영역을 추출하였다.

• Journal of KIISE:Software and Applications
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• v.29 no.3
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• pp.180-191
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• 2002

We present a new text localization method in images using a multi-layer perceptron(MLP) and a multiple continuously adaptive mean shift (MultiCAMShift) algorithm. An automatically constructed MLP-based texture classifier generates a text probability image for various types of images without an explicit feature extraction. The MultiCAMShift algorithm, which operates on the text probability Image produced by an MLP, can place bounding boxes efficiently without analyzing the texture properties of an entire image.

• The Journal of the Korea Contents Association
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• v.11 no.12
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• pp.511-521
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• 2011

As the use of image input and output devices increases, the importance of extracting text area in color images is also increasing. In this paper, in order to extract text area of the images efficiently, we present a text area extraction method for color images based on labeling and gradient difference method. The proposed method first eliminates non-text area using the processes of labeling and filtering. After generating the candidates of text area by using the property that is high gradient difference in text area, text area is extracted using the post-processing of noise removal and text area merging. The benefits of the proposed method are its simplicity and high accuracy that is better than the conventional methods. Experimental results show that precision, recall and inverse ratio of non-text extraction (IRNTE) of the proposed method are 99.59%, 98.65% and 82.30%, respectively.