Progress in the Raytheon BBN Arabic Offline Handwriting Recognition System

This paper presents the most recent progress and state of the art result obtained from BBN's Arabic offline handwriting recognition research. Our system is based a left-to-right hidden Markov model and integrates discriminative learning methods including discriminative MPE and n-best rescoring using the scores of glyph classifiers (SVM, DNN) and the RNNLM. Arabic-related features for n-best rescoring are also investigated in this paper. Multi-stage MAP/MLLR and writer verification are applied to adapt the recognizer in all training situations. Consensus network is extensively researched for system combination and improving challenging preprocessing problems.