Precise candidate selection for large character set recognition byconfidence evaluation
Liu, C.-L.
Nakagawa, M.
Central Res. Lab., Hitachi Ltd., Tokyo;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jun 2000
Volume: 22,
Issue: 6
On page(s): 636-641
ISSN: 0162-8828
References Cited: 0
CODEN: ITPIDJ
INSPEC Accession Number: 6693746
Digital Object Identifier: 10.1109/34.862202
Current Version Published: 2002-08-06
Abstract
This paper proposes a precise candidate selection method for large
character set recognition by confidence evaluation of distance-based
classifiers. The proposed method is applicable to a wide variety of
distance metrics and experiments on Euclidean distance and city block
distance have achieved promising results. By confidence evaluation, the
distribution of distances is analyzed to derive the probabilities of
classes in two steps: output probability evaluation and input
probability inference. Using the input probabilities as confidences,
several selection rules have been tested and the rule that selects the
classes with high confidence ratio to the first rank class produced best
results. The experiments were implemented on the ETL9B database and the
results show that the proposed method selects about one-fourth as many
candidates with accuracy preserved compared to the conventional method
that selects a fixed number of candidates
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