Hybrid Schemes of Homogeneous and Heterogeneous Classifiers for Cursive Word Recognition
نویسندگان
چکیده
Sophisticated hybrid schemes of the homogeneous and heterogeneous classifiers for cursive word recognition are presented. Two homogeneous MLPs (multi-layer perceptrons) are combined into a new single powerful classifier at the architectural level, and HMM (hidden Markov model) is added to the new classifier as a heterogeneous one at the output level. This is based on the idea that classifiers with more different methodologies and different features can better complement each other. The presented scheme achieves a recognition rate of 92.7% for English legal words of a CENPARMI database, a performance which is better than several previous hybrid schemes reported in the literature.
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