Modeling of contextual effects and its application to word spotting

We propose a model of spectral contextual e ects to simulate the superior recognition ability of humans and apply it to a front-end processor for word spotting. This model assumes that perceived spectra are in uenced by adjacent spectral peaks and that the magnitude of the in uence can be estimated by the minimum classi cation error criterion. Three experiments were carried out to evaluate the performance of the model. The results show that the model can compensate for neutralized spectra and bring them to their typical patterns. This improves word spotting accuracy.