2,938 research outputs found
Dictionary Matching with One Gap
The dictionary matching with gaps problem is to preprocess a dictionary
of gapped patterns over alphabet , where each
gapped pattern is a sequence of subpatterns separated by bounded
sequences of don't cares. Then, given a query text of length over
alphabet , the goal is to output all locations in in which a
pattern , , ends. There is a renewed current interest
in the gapped matching problem stemming from cyber security. In this paper we
solve the problem where all patterns in the dictionary have one gap with at
least and at most don't cares, where and are
given parameters. Specifically, we show that the dictionary matching with a
single gap problem can be solved in either time and
space, and query time , where is the number
of patterns found, or preprocessing time and space: , and query
time , where is the number of patterns found.
As far as we know, this is the best solution for this setting of the problem,
where many overlaps may exist in the dictionary.Comment: A preliminary version was published at CPM 201
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Word shape analysis for a hybrid recognition system
This paper describes two wholistic recognizers developed for use in a hybrid recognition system. The recognizers use information about the word shape. This information is strongly related to word zoning. One of the recognizers is explicitly limited by the accuracy of the zoning information extraction. The other recognizer is designed so as to avoid this limitation. The recognizers use very simple sets of features and fuzzy set based pattern matching techniques. This not only aims to increase their robustness, but also causes problems with disambiguation of the results. A verification mechanism, using letter alternatives as compound features, is introduced. Letter alternatives are obtained from a segmentation based recognizer coexisting in the hybrid system. Despite some remaining disambiguation problems, wholistic recognizers are found capable of outperforming the segmentation based recognizer. When working together in a hybrid system, the results are significantly higher than that of the individual recognizers. Recognition results are reported and compared
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