Machine Learning in Document Analysis and Recognition

Language: English

Published by Springer Berlin Heidelberg, 2010

3642095119 / 9783642095115

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Druck auf Anfrage Neuware - Printed after ordering - The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphicalcomponents of a document and to extract information. With rst papers dating back to the 1960's, DAR is a mature but still gr- ing research eld with consolidated and known techniques. Optical Character Recognition (OCR) engines are some of the most widely recognized pr- ucts of the research in this eld, while broader DAR techniques are nowadays studied and applied to other industrial and o ce automation systems. In the machine learning community, one of the most widely known - search problems addressed in DAR is recognition of unconstrained handwr- ten characters which has been frequently used in the past as a benchmark for evaluating machine learning algorithms, especially supervised classi ers. However, developing a DAR system is a complex engineering task that involves the integration of multiple techniques into an organic framework. A reader may feel that the use of machine learning algorithms is not approp- ate for other DAR tasks than character recognition. On the contrary, such algorithms have been massively used for nearly all the tasks in DAR. With large emphasis being devoted to character recognition and word recognition, other tasks such as pre-processing, layout analysis, character segmentation, and signature veri cation have also bene ted much from machine learning algorithms.

Seller Inventory # 9783642095115

Title
Machine Learning in Document Analysis and Recognition
Author
Hiromichi Fujisawa
Publisher
Springer Berlin Heidelberg
Publication year
2010
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3642095119
ISBN 13
9783642095115
Item weight
674 grams
Dimensions
235x155x25 mm

AHA-BUCH GmbH

Einbeck, Germany

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AbeBooks seller since August 14, 2006

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