• 한국멀티미디어학회논문지
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• 제13권12호
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• pp.1786-1797
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• 2010

Automatic understanding of contents in document image is a very hard problem due to involvement with mathematically challenging problems originated mainly from the over-determined system induced by document segmentation process. In both academic and industrial areas, there have been incessant and various efforts to improve core parts of content retrieval technologies by the means of separating out segmentation related issues using semi-structured document, e.g., invoice,. In this paper we proposed classification models for text lines on invoice document in which text lines were clustered into the five categories in accordance with their contents: purchase order header, invoice header, summary header, surcharge header, purchase items. Our investigation was concentrated on the performance of machine learning based models in aspect of linear-discriminant-analysis (LDA) and non-LDA (logic based). In the group of LDA, na$\"{\i}$ve baysian, k-nearest neighbor, and SVM were used, in the group of non LDA, decision tree, random forest, and boost were used. We described the details of feature vector construction and the selection processes of the model and the parameter including training and validation. We also presented the experimental results of comparison on training/classification error levels for the models employed.

• 한국통신학회논문지
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• 제14권6호
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• pp.663-674
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• 1989

본 논문은 문서자동 처리시스템의 구현을 위하여 문서영상의 패턴 분절과 구분처리 방법에 대하여 기술하였다. 가우스 분포함수의 1차 미분 연산자에 의한 윤곽선 추출과 체인 코드법에 의한 영상 분절, 2차 적률과 2차원 Rf 거리 (변환 영역)등에 의한 패턴 구분을 행하였다. 제한된 영상에 대하여 적용한 결과 문자 영역이나 지문, 사진, 도장 등 도형정보 영역을 잘 구분할 수 있음을 알았으며 사용된 알고리즘의 유용성을 검증할 수 있었다.

• Journal of Information Processing Systems
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• 제10권1호
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• pp.55-68
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• 2014

Automatic segmentation of foreground text from the background in degraded document images is very much essential for the smooth reading of the document content and recognition tasks by machine. In this paper, we present a novel approach to the binarization of degraded document images. The proposed method uses a new local contrast feature extracted based on the stroke width of text. First, a pre-processing method is carried out for noise removal. Text boundary detection is then performed on the image constructed from the contrast feature. Then local estimation follows to extract text from the background. Finally, a refinement procedure is applied to the binarized image as a post-processing step to improve the quality of the final results. Experiments and comparisons of extracting text from degraded handwriting and machine-printed document image against some well-known binarization algorithms demonstrate the effectiveness of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권10호
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• pp.4072-4091
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• 2015

A separation of text and non-text elements plays an important role in document layout analysis. A number of approaches have been proposed but the quality of separation result is still limited due to the complex of the document layout. In this paper, we present an efficient method for the classification of text and non-text components in document image. It is the combination of whitespace analysis with multi-layer homogeneous regions which called recursive filter. Firstly, the input binary document is analyzed by connected components analysis and whitespace extraction. Secondly, a heuristic filter is applied to identify non-text components. After that, using statistical method, we implement the recursive filter on multi-layer homogeneous regions to identify all text and non-text elements of the binary image. Finally, all regions will be reshaped and remove noise to get the text document and non-text document. Experimental results on the ICDAR2009 page segmentation competition dataset and other datasets prove the effectiveness and superiority of proposed method.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.469-472
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• 2003

Skew is inevitably occurred in a scanned document image Thus, character recognition systems are generally very sensitive to a skew angle. In this paper, we propose a robust slant correction algorithm based on dithering and estimating vortical transition. Character strings are segmented by projecting the vertical transition point and the slant corrected image. The segmentation method using the vertical transition point can effectively split the character strings touching vertically each other. Experimental results show that the proposed method has achieved robust slant correction and good performance of character string segmentation.

• 정보처리학회논문지B
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• 제16B권5호
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• pp.377-384
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• 2009

본 논문은 지역적 엔트로피 기반의 히스토그램을 이용한 문서영상의 분할과 텍스처 기반의 주성분 분석을 이용한 구성요소인 글자, 그림, 그래프 등의 구성요소 분류방안을 제안한다. 지역적 엔트로피와 히스토그램을 이용함으로써 문서영상의 다양한 변형이나 잡음에 강건하며 빠르고 손쉬운 이진화가 가능하다. 그리고 문서영상 내 존재하는 구성요소들이 각기 다른 텍스처 정보를 가지고 있다는 것에 착안하여 각 분할 영역의 텍스처 정보를 기반으로 주성분분석을 수행하였으며 이를 통해 사전에 구성요소들에 대한 구조정보를 설정할 필요가 없다는 장점을 가진다. 실험결과에서 다양한 문서영상의 분할 및 분류결과를 보였으며, 기존 방법보다 우수한 성능을 가져 그 유효함을 보였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 추계학술대회 논문집 학회본부
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• pp.201-204
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• 1991

In this paper the method of representing the document image by the quadtree data structure, and extracting each character seperately from the constructed quadtree are described. The document image is represented by a binary encoded quadtree and the segmentation is performed according to the information of each leaf node of the quadtree. Then, each character is extracted by the relation of positions of segments. This method enables to extract characters without examining every pixel in the image and the required storage of document image is decreased.

• International journal of advanced smart convergence
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• 제4권2호
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• pp.170-176
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• 2015

The stamp is one of the crucial information of traditional historical and cultural for nations. In this paper, we purpose to detect official stamps from scanned document and recognize the Mongolian traditional, historical stamps. Therefore we performed following steps: first, we detect official stamps from scanned document based on red-color segmentation and document standard. Then we collected 234 traditional stamp images with 6 classes and 100 official stamp images from scanned document images. Also we implemented the processing algorithms for noise removing, resize and reshape etc. Finally, we proposed a new scale invariant classification algorithm based on KNN (k-nearest neighbor). In the experimental result, our proposed a method had shown proper recognition rate.

• 정보처리학회논문지B
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• 제12B권3호
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• pp.357-364
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• 2005

본 논문에서는 두 단계 이미지 매칭을 이용하여 한글 문서영상에서 사용자 검색어를 빠르고 정확하게 검색할 수 있는 시스템을 제안한다. 본 시스템은 문자 분리, 검색어 영상 생성, 특징 추출 그리고 이미지 매칭 과정으로 구성된다. 매칭 과정에서 차원이 다른 두 가지 특징 벡터를 이용한다. 8쪽 분량의 문서 영상을 한국정보과학회 웹사이트에서 다운로드하였고, 그 문서로부터 1600개의 한글단어 영상을 획득하여 실험데이터로 사용하였다 그 결과 제안한 시스템은 기존에 제안된 영상-기반 한글 단어 검색 시스템보다 성능이 크게 향상되었음을 알 수 있었다.

• 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.