A method for connected hand-printed numeral recognition using hidden Markov models
Steve Procter, A.J. Elms, John Illingworth · 1998
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. 1 Introduction The authors have previously described the use of hidden Markov models (HMMs) for the recognition of noisy printed text [8--14] and have demonstrated how HMMs can be used to jointly segment and classify strings of connected characters which occur in, for example, faxed documents [15]. The same approach can be used for the recognition of strings of potentially connected hand-printed numerals such as ZIP codes. These n...