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

Abstract

Because of large shape variations in human handwriting, cursive handwriting recognition remains a challenging task. Usually, the recognition performance depends crucially upon the pre-processing steps, e.g. the word baseline detection and segmentation process. Hidden Markov models (HMMs) have the ability to model similarities and variations 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 the combination of the two recognition passes. Experiments recognizing cursive handwritten words with a 30,000-word lexicon have been carried out. The results demonstrate that our novel approaches achieve better recognition performance and reduce the relative error rate significantly.