A fuzzy-syntactic approach to allograph modeling for cursive scriptrecognition
Parizeau, M.
Plamondon, R.
Dept. de Genie Electr., Laval Univ., Que.;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: Jul 1995
Volume: 17,
Issue: 7
On page(s): 702-712
ISSN: 0162-8828
References Cited: 22
CODEN: ITPIDJ
INSPEC Accession Number: 5000961
Digital Object Identifier: 10.1109/34.391412
Current Version Published: 2002-08-06
Abstract
This paper presents an original method for creating allograph
models and recognizing them within cursive handwriting. This method
concentrates on the morphological aspect of cursive script recognition.
It uses fuzzy-shape grammars to define the morphological characteristics
of conventional allographs which can be viewed as basic knowledge for
developing a writer independent recognition system. The system uses no
linguistic knowledge to output character sequences that possibly
correspond to an unknown cursive word input. The recognition method is
tested using multi-writer cursive random letter sequences. For a test
dataset containing a handwritten cursive text 600 characters in length
written by ten different writers, average character recognition rates of
84.4% to 91.6% are obtained, depending on whether only the best
character sequence output of the system is considered or if the best of
the top 10 is accepted. These results are achieved without any
writer-dependent tuning. The same dataset is used to evaluate the
performance of human readers. An average recognition rate of 96.0% was
reached, using ten different readers, presented with randomized samples
of each writer. The worst reader-writer performance was 78.3%. Moreover,
results show that system performances are highly correlated with human
performances
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