Name
Abstract | ||
|---|---|---|
In this paper, we investigate the application of dynamic Bayesian networks (DBNs) to the recognition of degraded characters. DBNs are an extension of one-dimensional hidden Markov models (HMMs) which can handle several observation and state sequences. In our study, characters are represented by the coupling of two HMM architectures into a single DBN model. The interacting HMMs are a vertical HMM and a horizontal HMM whose observable outputs are the image columns and image rows, respectively. Various couplings are proposed where interactions are achieved through the causal influence between state variables. We compare non-coupled and coupled models on two tasks: the recognition of artificially degraded handwritten digits and the recognition of real degraded old printed characters. Our models show that coupled architectures perform more accurately on degraded characters than basic HMMs, the linear combination of independent HMM scores, as well as discriminative methods such as support vector machines (SVMs). |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.patcog.2008.03.022 | Pattern Recognition |
Keywords | DocType | Volume |
independent hmm score,horizontal hmm,basic hmms,degraded handwritten digit,degraded old printed character,vertical hmm,degraded character,interacting hmms,dynamic bayesian network,hmm architecture,image column,dynamic bayesian networks,hidden markov models,hidden markov model,support vector machine | Journal | 41 |
Issue | ISSN | Citations |
10 | Pattern Recognition | 18 |
PageRank | References | Authors |
0.75 | 28 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laurence Likforman-Sulem | 1 | 560 | 43.90 |
| Marc Sigelle | 2 | 316 | 34.12 |