Using Contextual Information to Selectively Adjust Preprocessing Parameters
It is generally accepted that some of the problems and ambiguities at the low level of recognition and processing can not be resolved without taking into account contextual expectations. However, in most recognition systems in use today, preprocessing is done using only bottom-up information. In this work we present a working system that is inspired by human perception and can use contextual information to modify preprocessing of local regions of the input pattern in order to improve recognition. This is especially useful, and often necessary, during the recognition of complex objects where changing the preprocessing of one section improves recognition of that section but has an adverse effect on the rest of the object. We present the results of our recognition system applied to on-line cursive script where in some cases the error rate is decreased by 20%.
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