Abstract:
Neural network classifier methods and decision trees
are widely used in various pattern recognition
research areas. Among them, printed character
recognition still faces some issues in all languages.
Myanmar sentences character recognition based on
Competitive Neural Trees (CNeT) is proposed in this
paper. CNeT performs hierarchical classification and
apply competitive unsupervised learning at node
label. The goals of Myanmar character recognition
are to obtain better recognition accuracy rate and
robust in geometric character shapes of different
styles. Three main steps such as preprocessing, shape
feature descriptors extraction and recognition are
implemented in our experiment. Shape feature
descriptors are extracted from preprocessed images
which are used in Competitive Neural Trees (CNeT)
recognition. This paper discusses a global search
method for the CNeT, which is utilized for training.
