Degraded documents recognition using pseudo-2D hidden Markov models in gray-scale images
Chinching Yen, Shyh-shiaw Kuo · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
The Pseudo 2D Hidden Markov Model (PHMM), which is an extension of the 1D HMM, has been shown to be an effective approach in recognition of highly degraded and connected text. In this paper, the PHMM is extended to directly recognize poorly-printed gray-level document images. The performance of the system is further enhanced by the N-best hypotheses search, coupled with duration constraint. Experimental results show that the new system has significantly improved the performance when compared to a similar system using threshold binary images as inputs. The recognition rate improves from 97.7% in binary system to 99.9% in gray-level with modified N-best search, over a testing set with similar blur and noise condition as the training set. For a much more degraded testing set, it improves from 89.59% to 98.51%. This also demonstrates the robustness of the proposed system.