Improving classication using a Condence Matrix based on weak classiers applied to OCR

This paper proposes a new feature representation method based on the construction of a Condence Matrix (CM). This representation consists of posterior probability values provided by several weak classiers, each one trained and used in dierent sets of features from the original sample. The CM allows the nal classier to abstract itself from discovering underlying groups of features. In this work the CM is applied to isolated character image recognition, for which several set of features can be extracted from each sample. Experimentation has shown that the use of the CM permits a signicant improvement in accuracy in most cases, while the others remain the same. The results were obtained after experimenting with four well-known corpora, using evolved meta-classiers with the k-Nearest Neighbor rule as weak classier and by applying statistical signicance tests.

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