Affiliations: Department of Computer Science, 131 Dodge Hall, Oakland University, Rochester, MI 48309, USA. E-mails: dbouchaffra@ieee.org; jtan@oakland.edu
Correspondence:
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Corresponding author: Professor Djamel Bouchaffra, Oakland University School of Engineering and Computer Science, 131 Dodge Hall, Rochester MI 48309, USA. Voice: +1 248 370 2242; URL: www.oakland.ed/~bouchaff.
Abstract: We introduce in this paper a generalization of the widely used hidden Markov models (HMM's), which we name “structural hidden Markov models” (SHMM). Our approach is motivated by the need of modeling complex structures which are encountered in many natural sequences pertaining to areas such as computational molecular biology, speech/handwriting recognition and content-based information retrieval. We consider observations as strings that produce the structures derived by an unsupervised learning process. These observations are related in the sense they all contribute to produce a particular structure. Four basic problems are assigned to a structural hidden Markov model: (1) probability evaluation, (2) state decoding, (3) structural decoding, and (4) parameter re-estimation. We have applied our methodology to recognize handwritten numerals. The results reported in this application show that the structural hidden Markov model outperforms the traditional hidden Markov model with a 23.9% error-rate reduction.
