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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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FD-Net: A Fully Dilated Convolutional Network for Historical document image Binarization 4th

FD-Net: A Fully Dilated Convolutional Network for Historical...

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4th Chinese Conference on Pattern Recognition and Computer Vision (PRCV)

作者： Xiong, Wei Yue, Ling Zhou, Lei Wei, Liying Li, Min Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Hubei Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29201 USA

ISBN: (纸本)9783030880040;9783030880033

Binarization is a key step in document analysis and archiving. The state-of-the-art models for document image binarization are variants of the encoder-and-decoder architecture, such as fully convolutional network (FCN) and U-Net. Despite their success, they still suffer from two challenges: (1) max-pooling or strided convolution reduces the spatial resolution of the intermediate feature maps, which may lead to information loss, and (2) interpolation or transposed convolution attempts to restore the feature maps to the desired spatial resolution, which may also result in pixelation. To overcome these two limitations, we propose a fully dilated convolutional network, termed FD-Net, using atrous convolutions instead of downsampling or upsampling operations. We have conducted extensive experiments on the recent DIBCO (document image binarization competition) and H-DIBCO (handwritten document image binarization competition) benchmark datasets. The experimental results show that our proposed FD-Net outperforms other state-of-the-art techniques by a large margin. The source code and pretrained models are publicly available at https://***/beargolden/ FD-Net.

关键词： Historical document image binarization document image segmentation Fully dilated convolutional network (FD-Net) Dilated convolution Atrous convolution

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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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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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A Novel Method for Text and Non-Text segmentation in document images

A Novel Method for Text and Non-Text Segmentation in Documen...

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2nd IEEE International Conference on Communications and Signal Processing (ICCSP)

作者： Deivalakshmi, S. Palanisamy, P. Vishwanathan, Gayatri Natl Inst Technol Elect & Commun Engn Dept Tiruchirappalli 620015 Tamil Nadu India

ISBN: (纸本)9781467348669;9781467348652

segmentation of the contents of document images into text and non-text regions is an essential pre-processing step for applications such as document analysis and classification, as well as OCR. This paper presents a novel technique to segment the document image into text and non-text regions using a combination of Wavelet-based Gray Level Co-Occurrence Matrix (GLCM) features and K-means clustering. A comparison between the performances of different wavelets in document image segmentation is also performed and tabulated. The technique was tested on a number of scanned article images from the MediaTeam document Database and results show a marked improvement over the already existing method based on GLCM features.

关键词： Discrete Wavelet Transform document image segmentation GLCM Features K-means Clustering Algorithm

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High Performance Layout Analysis of Medieval European document images 7

High Performance Layout Analysis of Medieval European Docume...

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7th International Conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Bukhari, Syed Saqib Gupta, Ashutosh Tiwari, Anil Kumar Dengel, Andreas German Res Ctr Artificial Intelligence Kaiserslautern Germany IITJ Jheepasani Rajasthan India Tech Univ Kaiserslautern Kaiserslautern Germany

ISBN: (纸本)9789897582769

Layout analysis, mainly including binarization and page segmentation, is one of the most important performance determining steps of an OCR system for complex medieval document images, which contain noise, distortions and irregular layouts. In this paper, we present high performance page segmentation techniques for medieval European document images which include a novel main-body and side-notes segregation and an improved version of OCRopus (OCRopus,) based text line extraction. In order to complete the high performance layout analysis pipeline, we have also presented the application of the percentile based binarization (Afzal et al., 2014) and the multiresolution morphology based text and non-text segmentation (Bukhari et al., 2011) methods over historical document images. presented layout analysis techniques are applied to a collection of the 15th century Latin document images, which achieved more than 90% accuracy for each of the segmentation techniques.

关键词： document Analysis Historical document Analysis Layout Analysis document image segmentation

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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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Text and Non-text segmentation based on Connected Component Features

Text and Non-text Segmentation based on Connected Component ...

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13th IAPR International Conference on document Analysis and Recognition (ICDAR)

作者： Viet Phuong Le Nayef, Nibal Visani, Muriel Ogier, Jean-Marc Cao De Tran La Rochelle Univ Fac Sci & Technol Lab L3I La Rochelle France Can Tho Univ Coll Informat & Commun Technol Can Tho Vietnam

ISBN: (纸本)9781479918058

document image segmentation is crucial to OCR and other digitization processes. In this paper, we present a learning-based approach for text and non-text separation in document images. The training features are extracted at the level of connected components, a mid-level between the slow noise-sensitive pixel level, and the segmentation-dependent zone level. Given all types, shapes and sizes of connected components, we extract a powerful set of features based on size, shape, stroke width and position of each connected component. Adaboosting with Decision trees is used for labeling connected components. Finally, the classification of connected components into text and non-text is corrected based on classification probabilities and size as well as stroke width analysis of the nearest neighbors of a connected component. The performance of our approach has been evaluated on the two standard datasets: UW-III and ICDAR-2009 competition for document layout analysis. Our results demonstrate that the proposed approach achieves competitive performance for segmenting text and non-text in document images of variable content and degradation.

关键词： decision trees document image processing feature extraction image classification image segmentation learning (artificial intelligence) text analysis Adaboosting ICDAR- 2009 competition OCR UW-III classification probabilities classification size connected component feature digitization processes document image segmentation document images document layout analysis learning-based approach noise-sensitive pixel level nontext segmentation nontext separation segmentation-dependent zone level stroke width analysis text segmentation text separation training feature extraction variable content Decision trees image segmentation Integrated circuits Optical character recognition software Photonics Decision Trees connected component optical character recognition softwares image segmentation text segmentation document image processing optical character recognition text processing document image Photonics image classification Feature extraction texts ecosystem-based approach Art Therapy

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Optické rozpoznávání znaků

Optické rozpoznávání znaků

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作者： Pokorný, Pavel Brno University of Technology

V této práci jsou představeny některé z metod pro vyhledání a rozpoznání textu v obraze. Zabývá se problematikou extrakce příznaků a představuje nejčastěji použí... 详细信息

V této práci jsou představeny některé z metod pro vyhledání a rozpoznání textu v obraze. Zabývá se problematikou extrakce příznaků a představuje nejčastěji používané algoritmy strojového učení. Popisuje postup při návrhu a implementaci aplikace určené k rozpoznávání tištěného textu a vytvoření datové sady znaků.

关键词： Rozpoznávání a vyhledávání textu v obraze strojové učení klasifikace výběr a extrakce příznaků. Optical character recognition OCR machine learning classification document image segmentation feature extraction. Text

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A Survey on document image Processing Methods Useful for Assistive Technology for the Blind

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INTERNATIONAL JOURNAL OF image AND GRAPHICS 2015年第1期15卷 1550005-1550005页

作者： Keefer, Robert Bourbakis, Nikolaos Pomiet LLC Dayton OH 45449 USA Wright State Univ Dayton OH 45449 USA

This paper offers a review of the state-of-the-art document image processing methods and their classification by identifying new trends for automatic document processing and understanding. document image processing (DIP) is an important problem related with most of the challenges coming from the image processing field and with applications to digital document summarization, readers for the visually impaired etc. Difficulties in the processing of documents can arise from lighting conditions, page curl, page rotation in 3D, and page layout segmentation. document image processing is usually performed in the context of higher-level applications that require an undistorted document image such as optical character recognition and document restoration/preservation. Typically, assumptions are made to constrain the processing problem in the context of a particular application. In this survey, we categorize document image processing methods on the basis of the technique, provide detailed descriptions of representative methods in each category, and examine their pros and cons. It important to notice here that the DIP field is broad, thus we try to provide a top-down/horizontal survey rather a bottom up. At the same time, we target the area of document readers for the blind, and use this application to guide us in a top-down survey of DIP. Moreover, we present a comparative survey based on important aspects of a marketable system that is dependent on document image processing techniques.

关键词： document image processing skew correction page curl correction document image segmentation

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