  Identification of minimum number of local regions of a handwritten character
image, containing well-defined discriminating features which are sufficient for
a minimal but complete description of the character is a challenging task. A
new region selection technique based on the idea of an enhanced Harmony Search
methodology has been proposed here. The powerful framework of Harmony Search
has been utilized to search the region space and detect only the most
informative regions for correctly recognizing the handwritten character. The
proposed method has been tested on handwritten samples of Bangla Basic,
Compound and mixed (Basic and Compound characters) characters separately with
SVM based classifier using a longest run based feature-set obtained from the
image subregions formed by a CG based quad-tree partitioning approach. Applying
this methodology on the above mentioned three types of datasets, respectively
43.75%, 12.5% and 37.5% gains have been achieved in terms of region reduction
and 2.3%, 0.6% and 1.2% gains have been achieved in terms of recognition
accuracy. The results show a sizeable reduction in the minimal number of
descriptive regions as well a significant increase in recognition accuracy for
all the datasets using the proposed technique. Thus the time and cost related
to feature extraction is decreased without dampening the corresponding
recognition accuracy.
