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Tenth International Conference on Computer Science and Information Technologies Revised Selected Papers CSIT

作者： Grishkin, Valery St Petersburg State Univ St Petersburg Russia

ISBN: (纸本)9781467375627

This paper proposes a method for segmentation of images containing both textual and graphical data. The method uses wavelet transformation to build the feature vector and a pattern recognition technique to classify areas of a document image. Values of wavelet coefficients distribution histogram of the source images sliding window serve as elements of a feature vector. For recognition of document area category, we use a trained classifier based upon support vector machine with RBF kernel.

关键词： image processing document image segmentation image recognition wavelet features

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document image segmentation Using Fuzzy Classifier and the Dual-Tree DWT

Document Image Segmentation Using Fuzzy Classifier and the D...

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14th International Computer Conference

作者： Saeedi, Jamal Safabakhsh, Reza Mozaffari, Saeed Amirkabir Univ Technol Dept Elect Engn Tehran Iran Amirkabir Univ Technol Dept Comp Engn Tehran Iran Semnan Univ Elect & Comp Dept Semnan Iran

ISBN: (纸本)9781424442614

In this paper, we propose a new method for textual areas extraction of an image using a fuzzy classifier and dual-tree discrete wavelet transform. We have extended our text extraction scheme for classification of document images into text, background, and picture components. Three class fuzzy classifiers and a morphological post-processing operation is used for this purpose. The proposed method shows better results compared to the previous wavelet-based document image segmentation algorithms.

关键词： document image segmentation dual-tree discrete wavelet transform fuzzy classifier morphological post-processing

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A method for combining complementary techniques for document image segmentation

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PATTERN RECOGNITION 2009年第12期42卷 3158-3168页

作者： Stamatopoulos, Nikolaos Gatos, Basilis Perantonis, Stavros J. Natl Ctr Sci Res Demokritos Inst Informat & Telecommun Computat Intelligence Lab Athens 15310 Greece

image segmentation is a major task of handwritten document image processing. Many of the proposed techniques for image segmentation are complementary in the sense that each of them using a different approach can solve different difficult problems such as overlapping, touching components, influence of author or font style etc. In this paper, a combination method of different segmentation techniques is presented. Our goal is to exploit the segmentation results of complementary techniques and specific features of the initial image so as to generate improved segmentation results. Experimental results on line segmentation methods for handwritten documents demonstrate the effectiveness of the proposed combination method. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： document image segmentation Combination method document image analysis segmentation

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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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Exploiting Spatial Attention and Contextual Information for document image segmentation 26th

Exploiting Spatial Attention and Contextual Information for ...

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26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)

作者： Sang, Yuman Zeng, Yifeng Liu, Ruiying Yang, Fan Yao, Zhangrui Pan, Yinghui Xiamen Univ Dept Automat Xiamen Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne Tyne & Wear England Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9783031059810;9783031059803

We propose a new framework of combining an attention mechanism with a conditional random field to deal with a document image segmentation task. The framework aims to recognize homogeneous regions, e.g. text, figures, or tables, in document images through a pixel-wise spatial attention module. The attention module obtains essential global information and gathers long-distance pixel dependencies. To get extra knowledge around images, we use a conditional random field to model contextual information in the document. The new framework enables an effective combination of pixel features with their contextual information in the document image segmentation task. We conduct extensive experiments over multiple challenging datasets and demonstrate the performance of our new framework in comparison to a series of state-of-the-art segmentation methods.

关键词： document image segmentation Conditional random field Spatial attention mechanism

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document image segmentation using Gabor wavelet and kernel-based methods

Document image segmentation using Gabor wavelet and kernel-b...

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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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Probabilistic homogeneity for document image segmentation

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PATTERN RECOGNITION 2021年 109卷 107591-107591页

作者： Lu, Tan Dooms, Ann Vrije Univ Brussel Fac Sci & Bioengn Sci Dept Math & Data Sci B-1050 Brussels Belgium

In this paper we propose a novel probabilistic framework for document segmentation exploiting human perceptual recognition of text regions from complicated layouts. In particular, we conceptualize text homogeneity as the Gestalt pattern displayed in text regions, characterized by proximately and symmetrically arranged units with similar morphological and texture features. We model this pattern in the local region of a connected component (CC) using an hierarchical formulation, which simulates a random walk-and-check on a graph encoding the neighborhood of the CC. The proposed formulation allows an effective com putation of what we call the probabilistic local text homogeneity (PLTH) using a weighted summation of the weights of the graph, which are derived from a probabilistic description of the homogeneity between neighboring CCs and computed through Bayesian cue integration. The proposed PLTH enables a multi-aspect analysis, where various primitives such as geometrical configuration, morphological features, texture characterization and location priors are integrated in one computational probabilistic model. This enables an effective text and non-text classification of CCs preceding any grouping process, which is currently absent in document segmentation. Experimental results show that our segmentation method based on the proposed PLTH model improves upon the state-of-the-art. (C) 2020 The Authors. Published by Elsevier Ltd.

关键词： Probabilistic local text homogeneity Random walk-and-check simulation Bayesian cue integration Text homogeneity pattern document image segmentation

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Using Multi-level segmentation Features for document image Classification 15th

Using Multi-level Segmentation Features for Document Image C...

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15th IAPR International Workshop on document Analysis Systems (DAS)

作者： Kaddas, Panagiotis Gatos, Basilis Natl Ctr Sci Res Demokritos Computat Intelligence Lab Inst Informat & Telecommun Athens 15310 Greece Univ Athens Dept Informat & Telecommun Athens 15784 Greece

ISBN: (纸本)9783031065552;9783031065545

document image classification is a crucial step in the processing pipeline for many purposes (e.g. indexing, OCR, keyword spotting) and is being applied at early stages. At this point, textual information about the document (OCR) is usually not available and additional features are required in order to achieve higher recognition accuracy. On the other hand, one may have reliable segmentation information (e.g. text block, paragraph, line, word, symbol segmentation results), extracted also at pre-processing stages. In this paper, visual features are fused with segmentation analysis results in a novel integrated workflow and end-to-end training can be easily applied. Significant improvements on popular datasets (Tobacco-3482 and RVL-CDIP) are presented, when compared to state-of-the-art methodologies which consider visual features.

关键词： document image classification document image segmentation Convolutional Neural Network Deep Learning

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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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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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