Offline recognition of handwritten numeral characters with polynomial neural networks using topological features

Authors:
El-Sayed M. El-Alfy
Affiliations:
College of Computer Sciences and Engineering, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
Venue:
AI'10 Proceedings of the 23rd Canadian conference on Advances in Artificial Intelligence
Year:
2010

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Abstract

Group-Method of Data Handling (GMDH) has been recognized as a powerful tool in machine learning It has the potential to build predictive neural network models of polynomial functions using only a reduced set of features which minimizes the prediction error This paper explores the offline recognition of isolated handwritten numeral characters described with non-Gaussian topological features using GMDH-based polynomial networks In order to study the effectiveness of the proposed approach, we apply it on a publicly available dataset of isolated handwritten numerals and compare the results with five other state-of-the-art classifiers: multilayer Perceptron, support-vector machine, radial-basis function, naïve Bayes and rule-based classifiers In addition to improving the classification accuracy and the per-class performance measures, using GMDH-based polynomial neural networks has led to significant feature dimensionality reduction.