TITLE:
A Comparison of Neural Classifiers for Graffiti Recognition
AUTHORS:
A. H. Al-Fatlawi, S. H. Ling, H. K. Lam
KEYWORDS:
Handwriting Recognition, Neural Networks, Network Structures
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.6 No.2,
May
2,
2014
ABSTRACT:
Technological
advances and the enormous flood of papers have motivated many researchers and
companies to innovate new technologies. In particular, handwriting recognition
is a very useful technology to support applications like electronic books
(eBooks), post code readers (that sort mails in post offices), and some bank
applications. This paper proposes three systems to discriminate handwritten
graffiti digits (0 to 9) and some commands with different architectures and
abilities. It introduces three classifiers, namely single neural network (SNN)
classifier, parallel neural networks (PNN) classifier and tree-structured
neural network (TSNN) classifier. The three classifiers have been designed
through adopting feed forward neural networks. In order to optimize the network
parameters (connection weights), the back-propagation algorithm has been used.
Several architectures are applied and examined to present a comparative study
about these three systems from different perspectives. The research focuses on
examining their accuracy, flexibility and scalability. The paper presents an
analytical study about the impacts of three factors on the accuracy of the
systems and behavior of the neural networks in terms of the number of the
hidden neurons, the model of the activation functions and the learning rate.
Therefore, future directions have been considered significantly in this paper
through designing particularly flexible systems that allow adding many more
classes in the future without retraining the current neural networks.