In this study, a hand writing recognition methodology based on the neural binary
micro-classifier network. The proposed methodology uses simple well known feature
extraction methodology. The feature extraction used is the discrete cosine
transformation low frequencies coefficients. The micro-classifier network is a
deterministic four layers neural network, the four layers are: input, micro-classifier,
counter, and output. The network provide confidence factor, and proper generalization
is guaranteed. Also, the network allows incremental learning, and more natural than
others. The recognition methodology was tested using the standard MNIST dataset. The
experimental results of the methodology showed comparative performance taking in
consideration the design advantages.