For digitizing or indexing physical documents, Optical Character Recognition
(OCR), the process of extracting textual information from scanned documents, is
a vital technology. When a document is visually damaged or contains non-textual
elements, existing technologies can yield poor results, as erroneous detection
results can greatly affect the quality of OCR. In this paper we present a
detection network dubbed BusiNet aimed at OCR of business documents. Business
documents often include sensitive information and as such they cannot be
uploaded to a cloud service for OCR. BusiNet was designed to be fast and light
so it could run locally preventing privacy issues. Furthermore, BusiNet is
built to handle scanned document corruption and noise using a specialized
synthetic dataset. The model is made robust to unseen noise by employing
adversarial training strategies. We perform an evaluation on publicly available
datasets demonstrating the usefulness and broad applicability of our model.

本文介绍了一个名为 BusiNet 的检测网络，旨在用于 OCR 商业文件。BusiNet 使用特殊的合成数据集处理扫描文档的损坏和噪音，并采用对抗性训练策略使模型对未见过的噪声具有鲁棒性。我们对公开数据集进行评估，证明其对 OCR 商业文件的有用性和广泛适用性。