Efficient single layer handwritten digit recognition through an optimizing algorithm

Handwritten digit recognition has attracted a great deal of research and analysis. However, only a few researchers wished to follow the track of linearity in finding a single layer model to classify the digits. The goals are clear from this approach; namely to avoid falling into the pitfalls of a representational space. This paper presents an improvement on a model that was presented earlier based on the decomposition of the input stream. A powerful predictive algorithm is used here to result in an error rate as low as 4% in a single layer network, which is an achievement to say the least. We then test the model by comparing its performance with a multi-layer back propagation network and a Least Mean Square algorithm to show how powerful it is. The power of decomposition may indeed be applicable in many different domains of pattern recognition.