Deformed systems for contextual postprocessing

doi:10.1016/S0165-0114(96)00304-1

1998-06-01

PRE

DOI：10.1016/S0165-0114(96)00304-1

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摘要

En 中文

In this paper a fuzzy method for contextual postprocessing, able to deal with the measurement level output that an isolated character classifier (ICC) can provide for every input letter, is introduced. The ICC information, is expressed as a fuzzy character which is then post-processed, by a deformed system, together with the rest of the fuzzy characters from a word. This deformed system implicitly contains the contextual knowledge provided by a dictionary and it is defined as an extension, for fuzzy inputs, of a classic automaton. Therefore, in the method, the classification of a character is postponed until the context is taken into account. This means that the classification and contextual processes are computed together. The formulation of the deformed systems makes possible the utilization of different strategies for the evidences composition. The method and also the composition strategies are evaluated in a text recognition experiment and high rates are obtained in correcting the characters miss-recognized by the ICC. Moreover, the results are compared with one of the best postprocessing methods and a clear improvement is achieved. (C) 1998 Elsevier Science B.V. All rights reserved.

Keyword:

artificial intelligence

image processing

linguistic modelling

pattern recognition

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期刊

Fuzzy Sets and Systems

IF:

2.7

论文数:

7.6K

被引数:

1.5W

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