Abstract
D.Ing. (Electrical and Electronic )
This thesis describes a neural network based system for the classification of handwritten digits as found
on real-life mail pieces. The proposed neural network uses a modular architecture which lends itself to
parallel implementation. This modular architecture is shown to produce adequate performance levels while
significantly reducing the required training time. The aim of the system is not only to achieve a high
recognition performance, but also to gain more insight into the functioning of the neural networks. This is
achieved by using separate feature extraction and classification stages. The output of the feature extraction
stage gives a good indication of the final performance level of the classifier, even before training. The need
for an optimal feature set is expressed to elevate the performance levels even further.