000 04158nam a22004815i 4500
001 978-3-540-76280-5
003 DE-He213
005 20170628034811.0
007 cr nn 008mamaa
008 100301s2008 gw | s |||| 0|eng d
020 _a9783540762805
_9978-3-540-76280-5
024 7 _a10.1007/978-3-540-76280-5
_2doi
050 4 _aTA329-348
050 4 _aTA640-643
072 7 _aTBJ
_2bicssc
072 7 _aMAT003000
_2bisacsh
082 0 4 _a519
_223
100 1 _aMarinai, Simone.
_eeditor.
245 1 0 _aMachine Learning in Document Analysis and Recognition
_h[electronic resource] /
_cedited by Simone Marinai, Hiromichi Fujisawa.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg,
_c2008.
300 _aXII, 434 p. 142 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-949X ;
_v90
505 0 _ato Document Analysis and Recognition -- Structure Extraction in Printed Documents Using Neural Approaches -- Machine Learning for Reading Order Detection in Document Image Understanding -- Decision-Based Specification and Comparison of Table Recognition Algorithms -- Machine Learning for Digital Document Processing: from Layout Analysis to Metadata Extraction -- Classification and Learning Methods for Character Recognition: Advances and Remaining Problems -- Combining Classifiers with Informational Confidence -- Self-Organizing Maps for Clustering in Document Image Analysis -- Adaptive and Interactive Approaches to Document Analysis -- Cursive Character Segmentation Using Neural Network Techniques -- Multiple Hypotheses Document Analysis -- Learning Matching Score Dependencies for Classifier Combination -- Perturbation Models for Generating Synthetic Training Data in Handwriting Recognition -- Review of Classifier Combination Methods -- Machine Learning for Signature Verification -- Off-line Writer Identification and Verification Using Gaussian Mixture Models.
520 _aThe 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.
650 0 _aEngineering.
650 0 _aArtificial intelligence.
650 0 _aEngineering mathematics.
650 1 4 _aEngineering.
650 2 4 _aAppl.Mathematics/Computational Methods of Engineering.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aFujisawa, Hiromichi.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783540762799
830 0 _aStudies in Computational Intelligence,
_x1860-949X ;
_v90
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-540-76280-5
912 _aZDB-2-ENG
999 _c21577
_d21577