Combination of Pruned Kohonen Maps for On-line Arabic Characters Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
On-line handwritten digit recognition based on trajectory and velocity modeling
Pattern Recognition Letters
On-line Arabic handwriting recognition system based on visual encoding and genetic algorithm
Engineering Applications of Artificial Intelligence
Online handwriting recognition for the Arabic letter set
CIT'11 Proceedings of the 5th WSEAS international conference on Communications and information technology
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Neural networks have been applied to various pattern classification and recognition problems for their learning ability, discrimination power, and generalization ability. The neural network most referenced in the pattern recognition literature are the multi-layer perceptron, the Kohonen associative memory and the Capenter-Grossberg ART network. The Kohonen memory runs an unsupervised clustering algorithm. It is easily trained and has attractive properties such as topological ordering and good generalization. In this study an on-line system for the recognition of handwriting Arabic characters using a Kohonen network is investigated. The input of the neural network is a feature vector of elliptic Fourier coefficients extracted from the handwritten dynamic representation. Experimental results show that the network successfully recognizes both clearly and roughly written characters with good performance.