This paper describes a user-assisted application to perform adaptive thresholding (i.e. binarization) on degraded handwritten documents. While existing adaptive thresholding techniques purport to be automatic, they in fact require the user to perform non-intuitive parameter tuning to obtain satisfactory results. In our work, we recast the problem into one where the user needs only to coarsely markup regions in the thresholded image that have unsatisfactory results. These regions are then segmented and processed locally - no parameter tuning is necessary. Our user study shows that not only do the majority of users prefer our application over parameter tuning, but our final results are better than existing algorithms due to the more targeted processing. While our main contribution is an effective user-assisted application for document binarization, we use this as an example to advocate the need to rethink how many computer vision solutions, notoriously reliant on parameter tuning, can be reworked to exploit meaningful user interaction.