The error rate can be considerably reduced on a style-consistent document if its style is identified and the right
style-specific classifier is used. Since in some applications both machines and humans have difficulty in identifying
the style, we propose a strategy to improve the accuracy of style-constrained classification by enlisting the human
operator to identify the labels of some characters selected by the machine. We present an algorithm to select the
set of characters that is likely to reduce the error rate on unlabeled characters by utilizing the labels to reclassify
the remaining characters. We demonstrate the efficacy of our algorithm on simulated data.