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Systems and Control in Aerospace and Astronautics, 2006. ISSCAA 2006. 1st International Symposium on

作者： Yu-Long Qiao Zhe-Ming Lu Chun-Yan Song Sheng-He Sun Department of Automatic Test and Control Harbin Institute of Technology Harbin China College of Information and Computer Engineering Northeast Forestry University Harbin China

ISBN: (纸本)0780393953

The document image segmentation is an important component in the document image understanding. kernel-based methods have demonstrated excellent performances in a variety of pattern recognition problems. This paper applies kernel-based methods and Gabor wavelet to the document image segmentation. The feature image are derived from Gabor filtered images. Taking the computational complexity into account, we subject the sampled feature image to spectral clustering algorithm (SCA). The clustering results serve as training samples to train a support vector machine (SVM). The initial segmentation is obtained by assigning class labels to pixels of the feature image with the trained SVM. A proper post-processing is used to improve the segmentation result. Several representative document images scanned from popular newspapers and journals are employed to verify the effectiveness of our algorithm.

关键词： Gabor filters computational complexity feature extraction image segmentation pattern clustering support vector machines Gabor wavelet method computational complexity document image segmentation filtered images kernel method pattern recognition problems s

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Two texture segmentation of document image using wavelet packet analysis

Two texture segmentation of document image using wavelet pac...

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9th International Conference on Advanced Communication Technology (ICACT 2007)

作者： Lee, Geum-Boon Odoyo, Wilfred O. Lee, Jae-Hoon Chung, Il-Yong Cho, Beom-Joon Chosun Univ Dept Comp Engn Kwangju South Korea

ISBN: (纸本)9788955191318

In this paper, we present a text segmentation method using wavelet packet analysis and k-means clustering algorithm. This approach assumes that the text and non-text regions are considered as two different texture regions. The text segmentation is achieved by using wavelet packet analysis as a feature analysis. The wavelet packet analysis is a method of wavelet decomposition that offers a richer range of possibilities for document image. From these multiscale features, we compute the local energy and intensify the features before adapting the k-means clustering algorithm based on the unsupervised learning rule. The results show that our text segmentation method is effective for document images scanned from newspapers and journals.

关键词： wavelet packet analysis document image segmentation k-means clustering algorithm energy estimation

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Simple and effective table detection system from document images

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INTERNATIONAL JOURNAL ON document ANALYSIS AND RECOGNITION 2006年第2-3期8卷 172-182页

作者： Mandal, S. Chowdhury, S. P. Das, A. K. Chanda, Bhabatosh Bengal Engn Coll DU CST Dept Howrah 711103 W Bengal India Indian Stat Unit ECS Unit Kolkata 700035 W Bengal India

The requirement of detection and identification of tables from document images is crucial to any document image analysis and digital library system. In this paper we report a very simple but extremely powerful approach to detect tables present in document pages. The algorithm relies on the observation that the tables have distinct columns which implies that gaps between the fields are substantially larger than the gaps between the words in text lines. This deceptively simple observation has led to the design of a simple but powerful table detection system with low computation cost. Moreover, mathematical foundation of the approach is also established including formation of a regular expression for ease of implementation.

关键词： table detection document image segmentation digital document library

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segmentation and enhancement of digital copies using a new fuzzy clustering method

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Conference on image Processing - Algorithms and Systems, Neural Networks, and Machine Learning

作者： Ahmed, Mohamed Nooman Cooper, Brian E. Lexmark Int Inc Appl Software Res Lexington KY USA

ISBN: (纸本)0819461040

In this paper, we introduce a new system to segment and label document images into text, halftoned images, and background using a modified fuzzy c-means (FCM) algorithm. Each pixel is assigned a feature vector, extracted from edge information and gray level distribution. The feature pattern is then assigned to a specific region using the modified fuzzy c-means approach. In the process of minimizing the new objective function. the neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in se,menting scans corrupted by scanner noise.

关键词： digital copying fuzzy c-means document image segmentation Markov random field

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Neuro-fuzzy segmentation of document images

Neuro-fuzzy segmentation of document images

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5th IASTED International Conference on Visualization, Imaging, and image Processing

作者： Górecki, P Castiello, C Caponetti, L Univ Bari Dipartimento Informat I-70126 Bari Italy

ISBN: (纸本)0889865280

The task of document image segmentation is to represent a digital image in a more interpretable form, recognising regions containing text, background and graphics. This paper presents a peculiar strategy for document image segmentation, where a neuro-fuzzy approach is involved. Firstly, image is segmented into text, graphics or background during a pixel level classification step. Successively, an analysis performed over the obtained regions is devoted to refine the initial segmentation results. A knowledge discovery process is applied to automatically derive from sample data the fuzzy rule bases, responsible of the inference scheme presiding over the classification of image pixels and regions. The proposed method proves to be accurate and robust to page skew and noise.

关键词： document analysis neuro-fuzzy classification document image segmentation feature extraction

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Content-based document enhancement by fuzzy clustering with spatial constraints

Content-based document enhancement by fuzzy clustering with ...

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Conference on Applications of Neural Networks and Machine Learning in image Processing IX

作者： Ahmed, MN Cooper, BE Lexmark Int Inc Appl Software Res Lexington KY USA

ISBN: (纸本)0819456462

In this paper, we present a new system to segment and label the contents of scanned documents as either text or image, using a modified fuzzy c-means (FCM) algorithm. Each pixel is assigned a feature pattern extracted from the gray level distribution and computed at different scales. The invariant feature pattern is then assigned to a specific region using fuzzy logic. Our algorithm is formulated by modifying the objective function of the standard FCM algorithm to allow the labeling of a pixel to be influenced by the labels in its immediate neighborhood. The neighborhood effect acts as a regularizer and biases the solution towards piecewise-homogeneous labelings. Such a regularization is useful in segmenting scans corrupted by scanner noise.

关键词： digital copying fuzzy c-means feature extraction document image segmentation

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Location of title and author regions in document images based on the Delaunay triangulation

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image AND VISION COMPUTING 2004年第4期22卷 319-329页

作者： Xiao, Y Yan, H Univ Sydney Sch Elect & Informat Engn Sydney NSW 2006 Australia City Univ Hong Kong Dept Comp Engn & Informat Technol Kowloon Hong Kong Peoples R China

Automatic title and author location can be a crucial step in journal document image processing systems. This paper presents a Delaunay triangulation-based method for identification of title and author areas in a technical document image. The positions and alignments of small text line regions are measured by different triangle groups and the character stroke widths are calculated from the constrained Delaunay triangulation. The rules defining spatial features and font attributes of the title and author region are applied to single line text regions to extract the title and author regions. Our experiment results show that the proposed method is effective. (C) 2003 Elsevier B.V. All fights reserved.

关键词： document image analysis document image segmentation connected component analysis location of title and author regions Delaunay triangulation

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Automated detection and segmentation of table of contents page and index pages from document images

Automated detection and segmentation of table of contents pa...

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12th International Conference on image Analysis and Processing

作者： Mandal, S Chowdhury, SP Das, AK Chanda, B BE Coll DU CST Dept Howrah 7111103 India

ISBN: (纸本)0769519482

The requirement of identifying and segmenting the table of contents (TOC) and index pages in the development of digital library is obvious. Digital document library is created to provide a non-labour intensive, cheap and flexible way of storing, representing and managing paper document in electronic form to facilitate indexing, viewing, printing and extracting the intended portions. Information from the TOC and index pages be extracted to use in document database for effective retrieval of the required pieces of information. In this paper we present fully auotmatic identification and segmentation of TOC and index pages from scanned document.

关键词： document image segmentation table of contents detection index page detection digital document library

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Context-based multiscale classification of document images using wavelet coefficient distributions

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IEEE TRANSACTIONS ON image PROCESSING 2000年第9期9卷 1604-1616页

作者： Li, J Gray, RM Xerox Corp Palo Alto Res Ctr Palo Alto CA 94304 USA Stanford Univ Dept Elect Engn Informat Syst Lab Stanford CA 94305 USA

In this paper, an algorithm is developed for segmenting document images into four classes: background, photograph, text, acid graph. Features used for classification are based on the distribution patterns of wavelet coefficients in high frequency bands. Two important attributes of the algorithm are its multiscale nature-it classifies an image at different resolutions adaptively, enabling accurate classification at class boundaries as well as fast classification overall-and its use of accumulated context information for improving classification accuracy.

关键词： context-dependent classification document image segmentation goodness of match multiscale classification text and photograph segmentation wavelet transform

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document image segmentation AND LAYOUT ANALYSIS

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 1994年第7期E77D卷 778-784页

作者： SAITOH, T YAMAAI, T TACHIKAWA, M Ricoh Information and Communication R&D Cent Yokohama-shi Japan

A system for segmentation of document image and ordering text areas is described, and applied to complex printed page layouts of both Japanese and English. There is no need to make any assumptions about the shape of blocks, hence the segmentation technique can handle not only skewed images without skew-correction but also documents where columns are not rectangular. In this technique, based on the bottom-up strategy, the connected components are extracted from the reduced image, and classified according to their local information. The connected components classified as characters are then merged into lines, and the lines are merged into areas. Extracted text areas are classified as body, caption, header or footer. A tree graph of the layout of the body texts is made, and the texts ordered by preorder traversal on the graph. We introduce the concept of an influence range of each node, a procedure for handling titles, thus obtaining good results on various documents. The total system is fast and compact.

关键词： document image segmentation LAYOUT ANALYSIS document image PROCESSING

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