Handbook of Document Image Processing and Recognition
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Beschreibung
The Handbook of Document Image Processing and Recognition provides a consistent, comprehensive resource on the available methods and techniques in document image processing and recognition. It includes unified comparison and contrast analysis of algorithms in standard table formats. Thus, it educates the reader in order to help them to make informed decisions on their particular problems.
The handbook is divided into several parts. Each part starts with an introduction written by the two editors. These introductions set the general framework for the main topic of each part and introduces the contribution of each chapter within the framework. The introductions are followed by several chapters written by established experts of the field.
Each chapter provides the reader with a clear overview of the topic and of the state of the art in techniques used (including elements of comparison between them). Each chapter is structured in the same way: It starts with an introductory text, concludes with a summary of the main points addressed in the chapter and ends with a comprehensive list of references. Whenever appropriate, the authors include specific sections describing and pointing to consolidated software and/or reference datasets. Numerous cross-references between the chapters ensure this is a truly integrated work, without unnecessary duplications and overlaps between chapters.
This reference work is intended for the use by a wide audience of readers from around the world such as graduate students, researchers, librarians, lecturers, professionals, and many other people.
This book serves as a comprehensive resource on the latest methods and techniques in document image processing and recognition for both experienced and novice researchers.
The Handbook of Document Image Processing and Recognition provides a consistent, comprehensive resource on the available methods and techniques in document image processing and recognition. It includes unified comparison and contrast analysis of algorithms in standard table formats. Thus, it educates the reader in order to help them to make informed decisions on their particular problems.
The handbook is divided into several parts. Each part starts with an introduction written by the two editors. These introductions set the general framework for the main topic of each part and introduces the contribution of each chapter within the framework. The introductions are followed by several chapters written by established experts of the field.
Each chapter provides the reader with a clear overview of the topic and of the state of the art in techniques used (including elements of comparison between them). Each chapter is structured in the same way: It starts with an introductory text, concludes with a summary of the main points addressed in the chapter and ends with a comprehensive list of references. Whenever appropriate, the authors include specific sections describing and pointing to consolidated software and/or reference datasets. Numerous cross-references between the chapters ensure this is a truly integrated work, without unnecessary duplications and overlaps between chapters.
This reference work is intended for the use by a wide audience of readers from around the world such as graduate students, researchers, librarians, lecturers, professionals, and many other people.
A comprehensive reference on the methods and techniques used in document image processing and recognition, for both experienced and novice researchers
Integrates and summarizes work on closely related tasks that are treated separately in the field
Includes unified comparison and contrast analyses of algorithms in standard table formats
Autor*in
David Doermann
Themen in »Handbook of Document Image Processing and Recognition«
Computer Vision Document Image Processing Human Computer Interaction Pattern Recognition
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From the book reviews:
“This edited compendium of chapters represents the largest effort to date to bring together the breadth and depth of image processing research for document text extraction, segmentation of document image into picture and text zones, and general optical character recognition (OCR) of the international family of foreign languages. … will appeal to the widest audience possible, including academicians, practitioners, library science and legal professionals, and all who are interested in the efficient storage and retrieval of vast numbers of documents.” (R. Goldberg, Computing Reviews, October, 2014)()