The recognition of handwritten digit strings of unknown length using hidden Markov models

We apply an HMM-based text recognition system to the recognition of handwritten digit strings of unknown length. The algorithm is tailored to the input data by controlling the maximum number of levels searched by the level building (LB) search algorithm. We demonstrate that setting this parameter according to the pixel length of the observation sequence, rather than using a fixed value for all input data, results in a faster and more accurate system. Best results were achieved by setting the maximum number of levels to twice the estimated number of characters in the input string. We also describe experiments which show the potential for further improvement by using an adaptive termination criterion in the LB search.