The Handbook of Document Image Processing and Recognition is a comprehensive resource on the latest methods and techniques in document image processing and recognition. Each chapter provides a clear overview of the topic followed by the state of the art of techniques used – including elements of comparison between them – along with supporting references to archival publications, for those interested in delving deeper into topics addressed. Rather than favor a particular approach, the text enables the reader to make an informed decision for their specific problems.
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.
Arvustused
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)
|
|
|
|
Part A Introduction, Background, Fundamentals |
|
|
1 | (132) |
|
1 A Brief History of Documents and Writing Systems |
|
|
3 | (8) |
|
|
|
2 Document Creation, Image Acquisition and Document Quality |
|
|
11 | (52) |
|
|
|
3 The Evolution of Document Image Analysis |
|
|
63 | (10) |
|
|
|
|
|
4 Imaging Techniques in Document Analysis Processes |
|
|
73 | (60) |
|
|
|
|
|
133 | (122) |
|
5 Page Segmentation Techniques in Document Analysis |
|
|
135 | (42) |
|
|
|
6 Analysis of the Logical Layout of Documents |
|
|
177 | (46) |
|
|
|
|
|
7 Page Similarity and Classification |
|
|
223 | (32) |
|
|
|
|
|
255 | (232) |
|
8 Text Segmentation for Document Recognition |
|
|
257 | (34) |
|
|
|
|
|
9 Language, Script, and Font Recognition |
|
|
291 | (40) |
|
|
|
|
|
10 Machine-Printed Character Recognition |
|
|
331 | (28) |
|
|
|
|
|
11 Handprinted Character and Word Recognition |
|
|
359 | (32) |
|
|
|
|
|
12 Continuous Handwritten Script Recognition |
|
|
391 | (36) |
|
|
|
|
|
13 Middle Eastern Character Recognition |
|
|
427 | (32) |
|
|
|
|
|
14 Asian Character Recognition |
|
|
459 | (28) |
|
|
|
|
|
|
|
|
Part D Processing of Non-textual Information |
|
|
487 | (216) |
|
15 Graphics Recognition Techniques |
|
|
489 | (34) |
|
|
|
|
|
16 An Overview of Symbol Recognition |
|
|
523 | (30) |
|
|
|
|
|
17 Analysis and Interpretation of Graphical Documents |
|
|
553 | (38) |
|
|
|
|
|
18 Logo and Trademark Recognition |
|
|
591 | (56) |
|
|
|
|
|
19 Recognition of Tables and Forms |
|
|
647 | (32) |
|
|
|
|
|
20 Processing Mathematical Notation |
|
|
679 | (24) |
|
|
|
|
|
|
|
703 | (182) |
|
21 Document Analysis in Postal Applications and Check Processing |
|
|
705 | (44) |
|
|
|
22 Analysis and Recognition of Music Scores |
|
|
749 | (26) |
|
|
|
|
|
23 Analysis of Documents Born Digital |
|
|
775 | (30) |
|
|
|
|
|
24 Image Based Retrieval and Keyword Spotting in Documents |
|
|
805 | (38) |
|
|
|
|
|
|
|
25 Text Localization and Recognition in Images and Video |
|
|
843 | (42) |
|
|
|
Part F Analysis of Online Data |
|
|
885 | (96) |
|
26 Online Handwriting Recognition |
|
|
887 | (30) |
|
|
|
|
|
27 Online Signature Verification |
|
|
917 | (32) |
|
|
|
|
|
|
|
|
|
949 | (32) |
|
|
|
|
|
Part G Evaluation and Benchmarking |
|
|
981 | (56) |
|
29 Datasets and Annotations for Document Analysis and Recognition |
|
|
983 | (28) |
|
|
|
30 Tools and Metrics for Document Analysis Systems Evaluation |
|
|
1011 | (26) |
|
|
|
|
| Index |
|
1037 | |
Dr. David Doermann is Senior Research Scientist and Director of the Laboratory for Language and Media Processing at the University of Maryland Institute for Advanced Computer Studies, College Park, MD, USA. He is also President and co-founder of Applied Media Analysis, Inc., and Editor-in-Chief of the International Journal on Document Analysis and Recognition.
Dr. Karl Tombre is Professor at Université de Lorraine, France, one of the lagest French universities, where he currently is vice-president in charge of partnerships and international affairs. He was one of the founders, and for many years an editor-in-chief of the International Journal on Document Analysis and Recognition. From 2007 to 2012 he was director of the Inria Nancy - Grand Est research center, a large public research center in computer science and applied mathematics. From 2006 to 2008 he was President of the International Association for Pattern Recognition (IAPR).
Lisainfo e-raamatute kohta