Progress in the Raytheon BBN Arabic Offline Handwriting Recognition System
Huaigu Cao, Prem Natarajan, Xujun Peng, Krishna Subramanian, David Belanger, Nan Li · 2014
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.