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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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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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document image segmentation Based on Wavelet Features 10

Document Image Segmentation Based on Wavelet Features

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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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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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Loss Function for Training Models of segmentation of document images

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PROGRAMMING AND COMPUTER SOFTWARE 2023年第7期49卷 574-589页

作者： Perminov, A. I. Turdakov, D. Yu. Belyaeva, O. V. Russian Acad Sci Ivannikov Inst Syst Programming Moscow 109004 Russia Moscow MV Lomonosov State Univ Moscow 119991 Russia

This work is devoted to improving the quality of segmentation of images of various scientific papers and legal acts by neural network models by training them using modified loss functions that take into account special features of images of the appropriate subject domain. The analysis of existing loss functions is carried out, and new functions are proposed that work both with the coordinates of bounding boxes and use information about the pixels of the input image. To assess the quality, a neural network segmentation model with modified loss functions is trained, and a theoretical assessment is carried out using a simulation experiment showing the convergence rate and segmentation error. As a result of the study, rapidly converging loss functions are created that improve the quality of document image segmentation using additional information about the input data.

关键词： document image segmentation loss functions modification of loss function

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HPSegNet: A Method for Handwritten and Printed Text Separation in document images

HPSegNet: A Method for Handwritten and Printed Text Separati...

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18th International Conference on document Analysis and Recognition (ICDAR)

作者： Chao, Yu Liu, Changsong Peng, Liangrui Wang, Yanwei Tsinghua Univ Dept Elect Engn Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China

ISBN: (纸本)9783031706417;9783031706424

The separation of handwritten and printed text in document images is an important task in the optical character recognition (OCR) research field. It is still a challenging problem to separate overlapped handwritten and printed text lines in images of complex documents including examination papers, legal documents, etc. In this paper, hand-written and printed text separation is formulated as a pixel-level document image segmentation task. Firstly, a modified Transformer-based model is designed for pixel-level document image segmentation. Secondly, a residual feature bypass is incorporated into the model to further exploit high-resolution features. Finally, a loss function combining focal loss and dice loss is designed to tackle the problem of imbalanced distributions of different classes. Experimental results on both a public English document image dataset and a self-built Chinese document image dataset have demonstrated the effectiveness of the proposed method.

关键词： Handwritten Text Printed Text document image segmentation Deep Learning

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Text Line segmentation in Historical Newspapers 21st

Text Line Segmentation in Historical Newspapers

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21st International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Lenc, Ladislav Martinek, Jiri Kral, Pavel Univ West Bohemia Fac Sci Appl Dept Comp Sci & Engn Plzen Czech Republic Univ West Bohemia Fac Sci Appl NTIS New Technol Informat Soc Plzen Czech Republic

ISBN: (纸本)9783031234798;9783031234804

This paper deals with page segmentation into individual text lines used as an input of a line-based OCR system. This task is usually solved in one step which directly identifies text lines in whole documents. However, a direct approach may jeopardize the reading order of the lines and thus deteriorate the overall transcription result. We propose a novel approach which decomposes this problem into two steps: text-block and text-line segmentation. The particular tasks are handled by algorithms based on fully convolutional neural networks. The proposed method is evaluated on two standard corpora, Europeana and RDCL 2019, and on a novel dataset created from data available in Porta fontium portal. This dataset is freely available for research purposes.

关键词： document image segmentation Layout analysis Fully convolutional network FCN

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An enhanced binarization framework for degraded historical document images

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EURASIP JOURNAL ON image AND VIDEO PROCESSING 2021年第1期2021卷 1-24页

作者： Xiong, Wei Zhou, Lei Yue, Ling Li, Lirong Wang, Song Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Hubei Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

Binarization plays an important role in document analysis and recognition (DAR) systems. In this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018), which is based on background estimation and energy minimization. First, we adopt mathematical morphological operations to estimate and compensate the document background. It uses a disk-shaped structuring element, whose radius is computed by the minimum entropy-based stroke width transform (SWT). Second, we perform Laplacian energy-based segmentation on the compensated document images. Finally, we implement post-processing to preserve text stroke connectivity and eliminate isolated noise. Experimental results indicate that the proposed method outperforms other state-of-the-art techniques on several public available benchmark datasets.

关键词： document image binarization document image segmentation Background estimation and compensation Laplacian energy minimization Minimum entropy-based stroke width transform (SWT) Markov random fields (MRFs) Graph cut

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Blind Decomposition of Multispectral document images Using Orthogonal Nonnegative Matrix Factorization

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IEEE TRANSACTIONS ON image PROCESSING 2021年 30卷 5997-6012页

作者： Rahiche, Abderrahmane Cheriet, Mohamed Univ Quebec Synchromedia Lab Ecole Technol Super ETS Montreal PQ H3C 1K3 Canada

This paper addresses the challenge of Multispectral (MS) document image segmentation, which is an essential step for subsequent document image analysis. Most previous studies have focused only on binary (text/non-text) separation. They also rely on handcrafted features and techniques dedicated to conventional images that do not take advantage of MS images' spectral richness. In this work, we reformulate this task as a source separation problem, whereby we target the blind decomposition of entire MS document images via a new orthogonal nonnegative matrix factorization (ONMF). On the one hand, we incorporate orthogonality constraint as a Riemannian optimization on the Stiefel manifold. On the other hand, based on which factor we impose the orthogonality constraint, i.e., either on the endmember matrix, abundance matrix, or both, we propose three ONMF models to investigate this issue and determine which model is more suitable for this study. Minimizing the three models subject to nonnegativity and orthogonality constraints simultaneously is very challenging. Therefore, we extend the alternating direction method of multipliers scheme to solve them. We evaluated our models on synthetic Hyperspectral (HS) images and real-world MS document images. The experimental results confirm the effectiveness of the proposed models and demonstrate their generalization power compared to state-of-the-art techniques.

关键词： Manifolds Optimization Data models Matrix decomposition Task analysis image segmentation image analysis Multispectral images orthogonal nonnegative matrix factorization Stiefel manifold document image segmentation Blind source separation

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Palmira: A Deep Deformable Network for Instance segmentation of Dense and Uneven Layouts in Handwritten Manuscripts 16th

Palmira: A Deep Deformable Network for Instance Segmentation...

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16th IAPR International Conference on document Analysis and Recognition (ICDAR)

作者： Sharan, S. P. Aitha, Sowmya Kumar, Amandeep Trivedi, Abhishek Augustine, Aaron Sarvadevabhatla, Ravi Kiran Int Inst Informat Technol Ctr Visual Informat Technol Hyderabad India

ISBN: (纸本)9783030863319

Handwritten documents are often characterized by dense and uneven layout. Despite advances, standard deep network based approaches for semantic layout segmentation are not robust to complex deformations seen across semantic regions. This phenomenon is especially pronounced for the low-resource Indic palm-leaf manuscript domain. To address the issue, we first introduce Indiscapes2, a new large-scale diverse dataset of Indic manuscripts with semantic layout annotations. Indiscapes2 contains documents from four different historical collections and is 150% larger than its predecessor, Indiscapes. We also propose a novel deep network PALMIRA for robust, deformation-aware instance segmentation of regions in handwritten manuscripts. We also report Hausdorff distance and its variants as a boundary-aware performance measure. Our experiments demonstrate that PALMIRA provides robust layouts, outperforms strong baseline approaches and ablative variants. We also include qualitative results on Arabic, South-East Asian and Hebrew historical manuscripts to showcase the generalization capability of PALMIRA.

关键词： Instance segmentation Deformable convolutional network Historical document analysis document image segmentation Dataset

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