Machine Learning in Document Analysis and Recognition
Machine Learning in Document Analysis and Recognition

Author: Simone Marinai

Publisher: Springer Science & Business Media

Published: 2008-01-10

Total Pages: 435

ISBN-13: 3540762795

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The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book is a collection of research papers and state-of-the-art reviews by leading researchers all over the world. It includes pointers to challenges and opportunities for future research directions. The main goal of the book is to identify good practices for the use of learning strategies in DAR.

Machine Learning in Document Analysis and Recognition
Machine Learning in Document Analysis and Recognition

Author: Simone Marinai

Publisher: Springer

Published: 2007-12-27

Total Pages: 436

ISBN-13: 3540762809

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The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book is a collection of research papers and state-of-the-art reviews by leading researchers all over the world. It includes pointers to challenges and opportunities for future research directions. The main goal of the book is to identify good practices for the use of learning strategies in DAR.

Document Processing Using Machine Learning
Document Processing Using Machine Learning

Author: Sk Md Obaidullah

Publisher: CRC Press

Published: 2019-11-25

Total Pages: 154

ISBN-13: 100073983X

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Document Processing Using Machine Learning aims at presenting a handful of resources for students and researchers working in the document image analysis (DIA) domain using machine learning since it covers multiple document processing problems. Starting with an explanation of how Artificial Intelligence (AI) plays an important role in this domain, the book further discusses how different machine learning algorithms can be applied for classification/recognition and clustering problems regardless the type of input data: images or text. In brief, the book offers comprehensive coverage of the most essential topics, including: · The role of AI for document image analysis · Optical character recognition · Machine learning algorithms for document analysis · Extreme learning machines and their applications · Mathematical foundation for Web text document analysis · Social media data analysis · Modalities for document dataset generation This book serves both undergraduate and graduate scholars in Computer Science/Information Technology/Electrical and Computer Engineering. Further, it is a great fit for early career research scientists and industrialists in the domain.

Document Analysis Systems
Document Analysis Systems

Author: Xiang Bai

Publisher: Springer Nature

Published: 2020-08-14

Total Pages: 588

ISBN-13: 3030570584

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This book constitutes the refereed proceedings of the 14th IAPR International Workshop on Document Analysis Systems, DAS 2020, held in Wuhan, China, in July 2020. The 40 full papers presented in this book were carefully reviewed and selected from 57 submissions. The papers are grouped in the following topical sections: character and text recognition; document image processing; segmentation and layout analysis; word embedding and spotting; text detection; and font design and classification. Due to the Corona pandemic the conference was held as a virtual event .

Document Image Analysis
Document Image Analysis

Author: Horst Bunke

Publisher: World Scientific

Published: 1994

Total Pages: 282

ISBN-13: 9810220464

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Interest in the automatic processing and analysis of document images has been rapidly increasing during the past few years. This book addresses the different subfields of document image analysis, including preprocessing and segmentation, form processing, handwriting recognition, line drawing and map processing, and contextual processing.

Modeling, Learning, and Processing of Text-Technological Data Structures
Modeling, Learning, and Processing of Text-Technological Data Structures

Author: Alexander Mehler

Publisher: Springer

Published: 2011-10-14

Total Pages: 398

ISBN-13: 3642226132

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Researchers in many disciplines have been concerned with modeling textual data in order to account for texts as the primary information unit of written communication. The book “Modelling, Learning and Processing of Text-Technological Data Structures” deals with this challenging information unit. It focuses on theoretical foundations of representing natural language texts as well as on concrete operations of automatic text processing. Following this integrated approach, the present volume includes contributions to a wide range of topics in the context of processing of textual data. This relates to the learning of ontologies from natural language texts, the annotation and automatic parsing of texts as well as the detection and tracking of topics in texts and hypertexts. In this way, the book brings together a wide range of approaches to procedural aspects of text technology as an emerging scientific discipline.

Handbook Of Character Recognition And Document Image Analysis
Handbook Of Character Recognition And Document Image Analysis

Author: Horst Bunke

Publisher: World Scientific

Published: 1997-05-02

Total Pages: 851

ISBN-13: 9814500380

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Optical character recognition and document image analysis have become very important areas with a fast growing number of researchers in the field. This comprehensive handbook with contributions by eminent experts, presents both the theoretical and practical aspects at an introductory level wherever possible.

Handwritten Historical Document Analysis, Recognition, and Retrieval - State of the Art and Future Trends
Handwritten Historical Document Analysis, Recognition, and Retrieval - State of the Art and Future Trends

Author: Andreas Fischer

Publisher: Machine Perception and Artific

Published: 2020-04-14

Total Pages: 240

ISBN-13: 9789811203237

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In recent years, libraries and archives all around the world have increased their efforts to digitize historical manuscripts. To integrate the manuscripts into digital libraries, pattern recognition and machine learning methods are needed to extract and index the contents of the scanned images. The unique compendium describes the outcome of the HisDoc research project, a pioneering attempt to study the whole processing chain of layout analysis, handwriting recognition, and retrieval of historical manuscripts. This description is complemented with an overview of other related research projects, in order to convey the current state of the art in the field and outline future trends. This must-have volume is a relevant reference work for librarians, archivists and computer scientists.

Handbook Of Pattern Recognition And Computer Vision (6th Edition)
Handbook Of Pattern Recognition And Computer Vision (6th Edition)

Author: Chi Hau Chen

Publisher: World Scientific

Published: 2020-04-04

Total Pages: 403

ISBN-13: 9811211086

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Written by world-renowned authors, this unique compendium presents the most updated progress in pattern recognition and computer vision (PRCV), fully reflecting the strong international research interests in the artificial intelligence arena.Machine learning has been the key to current developments in PRCV. This useful comprehensive volume complements the previous five editions of the book. It places great emphasis on the use of deep learning in many aspects of PRCV applications, not readily available in other reference text.

Document Analysis Systems II
Document Analysis Systems II

Author: Jonathan J. Hull

Publisher: World Scientific

Published: 1998

Total Pages: 542

ISBN-13: 9789810231033

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This book provides an overview of the state of the art in research and development of systems for document image analysis. Topics covered include a variety of systems and architectures for processing document images as well as methods for converting those images into formats that can be manipulated by a computer. The chapters are written by recognized experts in the field and describe Systems and Architectures, Recognition Techniques, Graphics Analysis, Document Image Retrieval, and World Wide Web Applications.