Offline handwritten numeral recognition using orthogonal Gaussian mixture model

In the statistical approach to offline handwritten numeral recognition, we use Gaussian mixture model (GMM) to approximate arbitrary class conditional probability density. For simplification, the GMM is assumed diagonal covariance matrixes. In case of the features of handwritten numerals are correlated statistically, a large number of mixture components are usually needed to obtain a good approximation. To solve this problem, the feature vectors are first transformed to the space spanned by the eigenvectors of the covariance matrix so that the correlation among the elements is reduced, namely orthogonal transformation. This GMM is defined as orthogonal Gaussian mixture model (OGMM). Finally, the effectiveness of this algorithm is demonstrated by applying it to the NIST database.