—Neural network classifier methods and decision
trees are widely used in various pattern recognition research
areas. Among them, handwritten character recognition still
faces some issues in all languages. Myanmar handwritten
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 handwritten character
recognition are to obtain better recognition accuracy rate and
robust in geometric character shapes of different writing 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) for recognition. This paper discusses a global
search method for the CNeT, which is utilized for training.
—Myanmar handwritten characters, CNeT,
global search method.
The authors are with University of Computer Studies, Yangon,
Myanmar (e-mail: firstname.lastname@example.org).
Cite:The authors are with University of Computer Studies, Yangon,
Myanmar (e-mail: email@example.com)., "Handwritten Character Recognition Using Competitive
Neural Trees," International Journal of Engineering and Technology vol. 5, no. 3, pp. 352-356, 2013.