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001 978-1-4471-2188-6
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005 20170628033620.0
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020 _a9781447121886
_9978-1-4471-2188-6
024 7 _a10.1007/978-1-4471-2188-6
_2doi
050 4 _aQ337.5
050 4 _aTK7882.P3
072 7 _aUYQP
_2bicssc
072 7 _aCOM016000
_2bisacsh
082 0 4 _a006.4
_223
100 1 _aPlötz, Thomas.
_eauthor.
245 1 0 _aMarkov Models for Handwriting Recognition
_h[electronic resource] /
_cby Thomas Plötz, Gernot A. Fink.
264 1 _aLondon :
_bSpringer London,
_c2011.
300 _aVI, 78p. 5 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5768
505 0 _aIntroduction -- General Architecture -- Markov Model Concepts: The Essence -- Markov Model Based Handwriting Recognition -- Recognition Systems for Practical Applications -- Discussion.
520 _aSince their first inception more than half a century ago, automatic reading systems have evolved substantially, thereby showing impressive performance on machine-printed text. The recognition of handwriting can, however, still be considered an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, so far, no standard procedures for building Markov-model-based recognizers could be established though trends toward unified approaches can be identified. Markov Models for Handwriting Recognition provides a comprehensive overview of the application of Markov models in the research field of handwriting recognition, covering both the widely used hidden Markov models and the less complex Markov-chain or n-gram models. First, the text introduces the typical architecture of a Markov model-based handwriting recognition system, and familiarizes the reader with the essential theoretical concepts behind Markovian models. Then, the text gives a thorough review of the solutions proposed in the literature for open problems in applying Markov model-based approaches to automatic handwriting recognition.
650 0 _aComputer science.
650 0 _aOptical pattern recognition.
650 1 4 _aComputer Science.
650 2 4 _aPattern Recognition.
700 1 _aFink, Gernot A.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781447121879
830 0 _aSpringerBriefs in Computer Science,
_x2191-5768
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4471-2188-6
912 _aZDB-2-SCS
999 _c16020
_d16020