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Computer Science - Image Processing | Handbook of Document Image Processing and Recognition

Handbook of Document Image Processing and Recognition

Doermann, David, Tombre, Karl (Eds.)

2014, XXI, 1055 p. 339 illus., 159 illus. in color. In 2 volumes, not available separately.

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  • Provides a comprehensive reference work on the latest methods and techniques in document image processing and recognition
  • Presents a clear overview of each topic followed by an explanation and comparison of techniques used
  • Enables readers to make informed decisions through unified comparison and contrast analyses of algorithms in standard table formats
  • Integrates and summarizes work on closely related tasks that are treated separately in the field of document image processing

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.

Content Level » Research

Keywords » Computer Vision - Document Image Processing - Human Computer Interaction - Pattern Recognition

Related subjects » Artificial Intelligence - General Issues - Image Processing

Table of contents 

A Brief History of Documents and Writing Systems.- Machine-printed Character Recognition.- Handprinted Character and Word Recognition.- Continuous Handwritten Script Recognition.- Middle Eastern Character Recognition.- Asian Character Recognition.- Graphics Recognition Techniques.- An Overview of Symbol Recognition.- Analysis and Interpretation of Graphical Documents.- Logo and Trademark Recognition.- Recognition of Tables and Forms.- Document Creation, Image Acquisition and Document Quality.- Processing Mathematical Notation.- Document Analysis in Postal Applications and Check Processing.- Analysis and Recognition of Music Scores.- Analysis of Documents Born Digital.- Image Based Retrieval and Keyword.-  Spotting in Documents.- Text Localization and Recognition in Images and Video.- Online Handwriting Recognition.- Online Signature Verification.- Sketching Interfaces.- Datasets and Annotations for Document Analysis and Recognition.- The Evolution of Document Image Analysis.- Tools and Metrics for Document Analysis Systems Evaluation.- Imaging Techniques in Document Analysis Processes.- Page Segmentation Techniques in Document Analysis.- Analysis of the Logical Layout of Documents.- Page Similarity and Classification.- Text Segmentation for Document Recognition.- Language, Script and Font Recognition.- Part A, Introduction, Background and Fundamentals.- Part B, Page Analysis.- Part C, Text Recognition.- Part D, Processing of Non-textual Information.- Part E, Applications.- Part F, Analysis of Online Data.- Part G, Evaluation and Benchmarking.

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