Multi-branch and two-pass HMM modeling approaches for off-line cursive handwriting recognition

Because of large shape variations in human handwriting, cursive handwriting recognition remains a challenging task. Usually the recognition performance depends crucially upon preprocessing steps, e.g. word baseline detection and segmentation process. Hidden Markov Models (HMMs) have the ability to model similarity and variation among samples of a class. In this paper we present a multi-branch HMM modeling method and an HMM based two-pass modeling approach. Whereas the multi-branch HMM method makes the resulting system more robust with word baseline detection, the two-pass recognition approach exploits the segmentation ability of the Viterbi algorithm and creates another HMM set and carries out a second recognition pass. The total performance is enhanced by combination of the two recognition passes. Experiments of recognizing cursive handwritten words with a 30000 words lexicon have been carried out. The results demonstrate that our novel approaches achieve better recognition performance and reduce the relative error rate significantly.