  Recent work has shown that by approximating the behaviour of a
non-differentiable black-box function using a neural network, the black-box can
be integrated into a differentiable training pipeline for end-to-end training.
This methodology is termed "differentiable bypass,'' and a successful
application of this method involves training a document preprocessor to improve
the performance of a black-box OCR engine. However, a good approximation of an
OCR engine requires querying it for all samples throughout the training
process, which can be computationally and financially expensive. Several
zeroth-order optimization (ZO) algorithms have been proposed in black-box
attack literature to find adversarial examples for a black-box model by
computing its gradient in a query-efficient manner. However, the query
complexity and convergence rate of such algorithms makes them infeasible for
our problem. In this work, we propose two sample selection algorithms to train
an OCR preprocessor with less than 10% of the original system's OCR engine
queries, resulting in more than 60% reduction of the total training time
without significant loss of accuracy. We also show an improvement of 4% in the
word-level accuracy of a commercial OCR engine with only 2.5% of the total
queries and a 32x reduction in monetary cost. Further, we propose a simple
ranking technique to prune 30% of the document images from the training dataset
without affecting the system's performance.
