Table of contents

  1. Front Matter
    Pages i-xxi
  2. Introduction, Background, Fundamentals

    1. Front Matter
      Pages 1-2
    2. Professor Henry S. Baird
      Pages 3-10
    3. PhDElisa H. Barney Smith
      Pages 11-61
    4. Henry S. Baird, Karl Tombre
      Pages 63-71
  3. Page Analysis

    1. Front Matter
      Pages 133-134
    2. Andreas Dengel, Faisal Shafait
      Pages 177-222
    3. Simone Marinai
      Pages 223-253
  4. Text Recognition

    1. Front Matter
      Pages 255-256
    2. Nicola Nobile, Ching Y. Suen
      Pages 257-290
    3. Umapada Pal, Niladri Sekhar Dash
      Pages 291-330
    4. Huaigu Cao, Prem Natarajan
      Pages 331-358
    5. Sergey Tulyakov, Venu Govindaraju
      Pages 359-389
    6. Volkmar Frinken, Horst Bunke
      Pages 391-425
    7. Abdel Belaı̈d, Mohamed Imran Razzak
      Pages 427-457
    8. Srirangaraj Setlur, Zhixin Shi
      Pages 459-486
  5. Processing of Non-textual Information

    1. Front Matter
      Pages 487-488
    2. Josep Lladós, Marçal Rusiñol
      Pages 489-521
    3. Salvatore Tabbone, Oriol Ramos Terrades
      Pages 523-551
    4. Bart Lamiroy, Jean-Marc Ogier
      Pages 553-590
    5. Anastasios Kesidis, Dimosthenis Karatzas
      Pages 591-646
    6. Bertrand Coüasnon, Aurélie Lemaitre
      Pages 647-677
    7. Dorothea Blostein, Richard Zanibbi
      Pages 679-702
  6. Applications

    1. Front Matter
      Pages 703-704
    2. Alicia Fornés, Gemma Sánchez
      Pages 749-774
    3. Jianying Hu, Ying Liu
      Pages 775-804
    4. Chew Lim Tan, Xi Zhang, Linlin Li
      Pages 805-842
  7. Analysis of Online Data

    1. Front Matter
      Pages 885-885
    2. JinHyung Kim, Bong-Kee Sin
      Pages 887-915
    3. Réjean Plamondon, Giuseppe Pirlo, Donato Impedovo
      Pages 917-947
    4. Tong Lu, Liu Wenyin
      Pages 949-980
  8. Evaluation and Benchmarking

    1. Front Matter
      Pages 981-981
    2. Volker Märgner, Haikal El Abed
      Pages 1011-1036
  9. Umapada Pal, Niladri Sekhar Dash
    Pages E1-E1
  10. Back Matter
    Pages 1037-1055

About this book


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.


Computer Vision Document Image Processing Human Computer Interaction Pattern Recognition

Editors and affiliations

  • David Doermann
    • 1
  • Karl Tombre
    • 2
  1. 1.University of MarylandCollege ParkMDUSA
  2. 2.Université de LorraineNancyFrance

Bibliographic information