Research On Hidden Markov Model And Its Application To Image Recognition

The technology of image recognition is widely used in lives and industries. The recognition of complex images is now still the focus of the research on pattern recognition and image processing. This paper focuses on the research of image recognition based on the hidden Markov model (HMM). Recent years, people have put great effort into the research on how to introduce HMM to image recognition successfully that has been widely applied in speech recognition. This paper considers it the most feasible and efficient way to build proper pseudo 2D HMM for distinct object. This paper first proposes a nested pseudo 2D HMM for off-line handwritten Chinese character recognition. The nested pseudo 2D topological structure is a reasonable simplification of a truly 2D one. This simplification is based on an in-depth study of the Chinese character structure and a full-scale overview of the relationship of adjacent points and areas. The proposed model has a recognition rate of 91.8% and a top-ten correct rate of 95.2% on GB first-level character database. This paper also illustrates a similar research on face recognition and a simplified pseudo 2D HMM is put forward. This model has a high recognition rate of 99.5% on ORL face database and a low computational complexity in comparison with other face HMMs. Based on the questions of HMM brought by the above two applications, this paper points out the limit of fixed state set in classic HMM theory. Therefore, this paper proposes a novel self-adaptive HMM, which can be denoted by θ(N,A,B,π). It has a variable state set, for the purpose of automatically matching the "true" hidden state set and extracting more structure information of the signal being modeled. It employs a shrink training algorithm base on the deterministic annealing (DA) global optimization technique. This training algorithm adopts a maximum a posteriori (MAP) optimization criterion. Experimental results indicate that the self-adaptive model is useful in both the theory and application: it matches the inherent structure of signals better, can extract more structure information and can improve the performance of HMM in both stochastic signal modeling and image recognition applications. After the self-adaptive redesign, the proposed nested pseudo 2D HMM of the off-line handwritten Chinese character can increase the recognition rate to 95.9% and the top-ten correct rate to 99.0%.