Abstract
A method for the recognition of hand-printed numerals using hidden Markov models is described. The method involves the representation of 2D images of a character with two 1D models, one for the pixel columns of the image and the other for the rows. Various normalisations are applied to both the training and test data to reduce variations between characters within a class, resulting in a corresponding improvement in classification performance. In our latest experiments, a character recognition rate of over 93% was achieved on digit strings of variable length.