Off-line recognition of handwritten Arabic words using multiple hidden Markov models

摘要

A complete scheme for unconstrained Arabic handwritten word recognition based on a multiple hidden Markov models (HMM) is presented. The overall engine of this combination of a global feature scheme with a HMM module, is a system able to classify Arabic-handwritten words. The system first removes some of the variation in the images. Next, it codes the skeleton and edge of the word such that features are extracted. Then, a rule-based classifier is used as a global recognition engine. Finally, for each group, the HMM approach is used for trial classification. The output is a word in the lexicon