A neuro-fuzzy system for isolated hand-written digit recognition using a similarity fuzzy measure is presented. The system is composed of two main blocks: a first block that normalizes the input and compares it with a set of fuzzy patterns, and a second block with a multilayer perceptron to perform a neuronal classification. The comparison with the fuzzy patterns is performed via a fuzzy similarity measure that uses the Yager parametric t-norms and t-conorms. Along this work, several values of the parameters have been studied, in order to obtain the best classification. The simplicity of the method makes it extremely quick and provides a recognition accuracy about 90% in classification of isolated digits, making it an attractive method for practical applications.
@article{urn:eudml:doc:39227,
title = {A neuro-fuzzy system for isolated hand-written digit recognition.},
journal = {Mathware and Soft Computing},
volume = {8},
year = {2001},
pages = {291-301},
zbl = {0996.68179},
language = {en},
url = {http://dml.mathdoc.fr/item/urn:eudml:doc:39227}
}
Pinzolas, Miguel; Astrain, José Javier; Villadangos, Jesús; González de Mendívil, José Ramón. A neuro-fuzzy system for isolated hand-written digit recognition.. Mathware and Soft Computing, Tome 8 (2001) pp. 291-301. http://gdmltest.u-ga.fr/item/urn:eudml:doc:39227/