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231224s2012 xx |||||o 00| ||eng c |
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|a 10.1109/TPAMI.2011.113
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|a eng
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|a Frinken, Volkmar
|e verfasserin
|4 aut
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|a A novel word spotting method based on recurrent neural networks
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|c 2012
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 21.05.2012
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|a Date Revised 01.03.2012
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a Keyword spotting refers to the process of retrieving all instances of a given keyword from a document. In the present paper, a novel keyword spotting method for handwritten documents is described. It is derived from a neural network-based system for unconstrained handwriting recognition. As such it performs template-free spotting, i.e., it is not necessary for a keyword to appear in the training set. The keyword spotting is done using a modification of the CTC Token Passing algorithm in conjunction with a recurrent neural network. We demonstrate that the proposed systems outperform not only a classical dynamic time warping-based approach but also a modern keyword spotting system, based on hidden Markov models. Furthermore, we analyze the performance of the underlying neural networks when using them in a recognition task followed by keyword spotting on the produced transcription. We point out the advantages of keyword spotting when compared to classic text line recognition
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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|a Fischer, Andreas
|e verfasserin
|4 aut
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|a Manmatha, R
|e verfasserin
|4 aut
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|a Bunke, Horst
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 34(2012), 2 vom: 07. Feb., Seite 211-24
|w (DE-627)NLM098212257
|x 1939-3539
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|g year:2012
|g number:2
|g day:07
|g month:02
|g pages:211-24
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