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.

翻译：近期研究表明，通过使用神经网络近似不可微黑箱函数的行为，可将黑箱集成到可微训练流程中实现端到端训练。该方法被称为"可微旁路"，其成功应用包括训练文档预处理模块以提升黑箱OCR引擎的性能。然而，要获得OCR引擎的良好近似，需在训练过程中对所有样本进行查询，这会产生高昂的计算与财务成本。尽管黑箱攻击领域的零阶优化算法可通过查询高效方式计算梯度来生成对抗样本，但其查询复杂度与收敛速率不适用于本问题。本文提出两种样本选择算法，使得OCR预处理器的训练仅需使用原始系统OCR引擎查询量的10%以下，即可在保持精度无显著损失的前提下将总训练时间缩减超过60%。我们进一步证明，仅使用2.5%的总查询量即可使商业OCR引擎的词级准确率提升4%，同时将货币成本降低32倍。此外，我们提出一种简单排序技术，可在不影响系统性能的情况下从训练数据集中修剪30%的文档图像。