  The problem of correctly segmenting touching characters is an hard task to
solve and it is of major relevance in pattern recognition. In the recent years,
many methods and algorithms have been proposed; still, a definitive solution is
far from being found. In this paper, we propose a novel method based on fuzzy
logic. The proposed method combines in a novel way three features for
segmenting touching characters that have been already proposed in other studies
but have been exploited only singularly so far. The proposed strategy is based
on a 3--input/1--output fuzzy inference system with fuzzy rules specifically
optimized for segmenting touching characters in the case of Latin printed and
handwritten characters. The system performances are illustrated and supported
by numerical examples showing that our approach can achieve a reasonable good
overall accuracy in segmenting characters even on tricky conditions of touching
characters. Moreover, numerical results suggest that the method can be applied
to many different datasets of characters by means of a convenient tuning of the
fuzzy sets and rules.
