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Machine Learning in Document Analysis and Recognition
. Ed(S): Marinai, Simone; Fujisawa, Hiromichi
€ 195.54
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Description for Machine Learning in Document Analysis and Recognition
paperback. Editor(s): Marinai, Simone; Fujisawa, Hiromichi. Series: Studies in Computational Intelligence. Num Pages: 434 pages, 142 black & white illustrations, 41 black & white tables, biography. BIC Classification: PBW; UYQM. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 23. Weight in Grams: 682.
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 ... Read more
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 ... Read more
Product Details
Format
Paperback
Publication date
2010
Publisher
Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Germany
Number of pages
434
Condition
New
Series
Studies in Computational Intelligence
Number of Pages
434
Place of Publication
Berlin, Germany
ISBN
9783642095115
SKU
V9783642095115
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
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