  This paper deals with a new method for recognition of offline Handwritten
non-compound Devnagari Characters in two stages. It uses two well known and
established pattern recognition techniques: one using neural networks and the
other one using minimum edit distance. Each of these techniques is applied on
different sets of characters for recognition. In the first stage, two sets of
features are computed and two classifiers are applied to get higher recognition
accuracy. Two MLP's are used separately to recognize the characters. For one of
the MLP's the characters are represented with their shadow features and for the
other chain code histogram feature is used. The decision of both MLP's is
combined using weighted majority scheme. Top three results produced by combined
MLP's in the first stage are used to calculate the relative difference values.
In the second stage, based on these relative differences character set is
divided into two. First set consists of the characters with distinct shapes and
second set consists of confused characters, which appear very similar in
shapes. Characters of distinct shapes of first set are classified using MLP.
Confused characters in second set are classified using minimum edit distance
method. Method of minimum edit distance makes use of corner detected in a
character image using modified Harris corner detection technique. Experiment on
this method is carried out on a database of 7154 samples. The overall
recognition is found to be 90.74%.
