In this work, discriminative training is studied to improve the performance of our pseudo two-dimensional (2-D) hidden Markov model (PHMM) based text recognition system. The aim of this discriminative training is to adjust model parameters to directly minimize the classification error rate. Experimental results have shown great reduction in recognition error rate even for PHMMs already well-trained using conventional maximum likelihood (ML) approaches.

The pseudo two dimensional hidden Markov model (PHMM) is extended to directly recognize poorly-printed gray-scale document images. The N-best hypotheses search, coupled with duration correction, is also developed to find best candidates. Experimental results have demonstrated that the performance of the new system has been significantly improved when compared to the original PHMM system [Kuo and Agazzi, 1994] using binary images as inputs. The recognition rate improves from 97.7% to 100%, over a testing set with similar blur and noise conditions as the training set. For a testing range far outside the training one, it improves from 89.59% to 98.51%, which also demonstrates the robustness of the proposed system.