Handbook of Document Image Processing and Recognition - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Books Year : 2014

Handbook of Document Image Processing and Recognition


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.

Dates and versions

hal-00995696 , version 1 (23-05-2014)



David Doermann, Karl Tombre. Handbook of Document Image Processing and Recognition. Springer, pp.1055, 2014, 978-0-85729-858-4. ⟨10.1007/978-0-85729-859-1⟩. ⟨hal-00995696⟩
299 View
0 Download



Gmail Facebook X LinkedIn More