Handwritten character recognition using the hidden Markov model (HMM) has been an active research topic for the past decade. One of the major problems

however

is that the handwritten characters may not exhibit consistent patterns due to different people's different writing styles. To enhance HMM's encoding stability and to reduce its modeling complexity

we propose a new approach in this paper. Specifically

we first obtain a set of uncorrelated optimal discriminant vectors by conducting feature extraction and dimension reduction using the uncorrelated Foley-Sammon transformation. Next

using a new feature space spanned by the optimal discriminant vectors

we obtain the projection coefficients of the raw data onto this new feature space. We then use these coefficients to form the observation sequence of the HMM. Because the uncorrelated Foley-Sammon transformation ensures minimum intra-class distance and maximum inter-class distance

it significantly improves HMM's encoding stability and difference classes' separability. In fact

the transformation allows different characters to be separable in many projection directions. To validate the accuracy and robustness of the proposed approach

we conduct experiments on the widely used US Postal Service (USPS) data set. Experiments show that the integration of the uncorrelated Foley-Sammon transformation and the HMM performs very well

achieving a recognition rate of 92%. It not only is better than regular HMM

but also is superior to the widely used nerual network based approaches.