Online character recognition based on elastic matching and quadratic discrimination

We try to link elastic matching with a statistical discrimination framework to overcome the overfitting problem which often degrades the performance of elastic matching-based online character recognizers. In the proposed technique, elastic matching is used just as an extractor of a feature vector representing the difference between input and reference patterns. Then quadratic discrimination is performed under the assumption that the feature vector is governed by a Gaussian distribution. The result of a recognition experiment on UNIPEN database (Train-R01/V07, 1a) showed that the proposed technique can attain a high recognition rate (97.95%) and outperforms a recent elastic matching-based recognizer.