A string length predictor to control the level building of HMMs for handwritten numeral recognition

In this paper a two-stage HMM-based method for recognizing handwritten numeral strings is extended to work with handwritten numeral strings of unknown length. We have proposed a Bayesian-based string length predictor (SLP) to estimate the number of digits in a string taking into account its width in pixels. The top 3 decisions of the SLP module are used to control the maximum number of levels to be searched by the Level Building (LB) algorithm. On 12,802 handwritten numeral strings and 2,069 touching digit pairs, this strategy has shown a small loss. (0.91%) in terms of recognition performance compared to the results when the string length is considered as known.