Articles liés à Machine Learning in Document Analysis and Recognition
Synopsis
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.
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Présentation de l'éditeur
The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book is a collection of research papers and state-of-the-art reviews by leading researchers all over the world. It includes pointers to challenges and opportunities for future research directions. The main goal of the book is to identify good practices for the use of learning strategies in DAR.
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Machine Learning in Document Analysis and Recognition (Studies in Computational Intelligence, 90)
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Machine Learning in Document Analysis and Recognition (Studies in Computational Intelligence, 90)
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Machine Learning in Document Analysis and Recognition
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Springer Berlin Heidelberg Nov 2010, 2010
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ISBN 13 : 9783642095115
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The objective of Document Analysis and Recognition (DAR) is to recognize the text and graphical components of a document and to extract information. This book is a collection of research papers and state-of-the-art reviews by leading researchers all over the world. It includes pointers to challenges and opportunities for future research directions. The main goal of the book is to identify good practices for the use of learning strategies in DAR. 448 pp. Englisch. N° de réf. du vendeur 9783642095115
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Machine Learning in Document Analysis and Recognition (Studies in Computational Intelligence, 90)
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Machine Learning in Document Analysis and Recognition
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Machine Learning in Document Analysis and Recognition
Edité par
Springer Berlin Heidelberg, Springer Berlin Heidelberg Nov 2010, 2010
ISBN 10 : 3642095119
ISBN 13 : 9783642095115
Neuf
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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 448 pp. Englisch. N° de réf. du vendeur 9783642095115
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Machine Learning in Document Analysis and Recognition
Edité par
Springer Berlin Heidelberg, 2010
ISBN 10 : 3642095119
ISBN 13 : 9783642095115
Neuf
Taschenbuch
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Taschenbuch. Etat : Neu. 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. N° de réf. du vendeur 9783642095115
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Machine Learning in Document Analysis and Recognition (Studies in Computational Intelligence)
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