Abstract:
Competitive Neural Trees (CNeT) are widely used for
classification in pattern recognition. This paper
applies this technique for recognizing of Myanmar
handwritten characters. This paper involves three
important steps, typically preprocessing, feature
extraction and classification. The aim of
preprocessing is to improve the quality of the images
for further processing. For the extraction of features,
four of the 3×3 masks are applied to word images to
extract horizontal, vertical, right and left-diagonal
lines. Afterwards, decomposed images should be
partitioned to eight sectors around the center of
image and the number of black pixels in each sector
calculated and normalized by dividing them upon the
total number of black pixels in word images for
feature vector. These feature vector from word images
which are used in Competitive Neural Trees (CNeT)
for recognition purpose. This paper introduces a
global search method for the CNeT, which is utilized
for training.
