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。

BusiNet -- 业务文档轻量快速文本检测网络

BusiNet -- a Light and Fast Text Detection Network for Business Documents

Inductive Logic Programming (ILP) is a form of machine learning (ML) which in
contrast to many other state of the art ML methods typically produces highly
interpretable and reusable models. However, many ILP systems lack the ability
to naturally learn from any noisy or partially misclassified training data. We
introduce the relaxed learning from failures approach to ILP, a noise handling
modification of the previously introduced learning from failures (LFF) approach
which is incapable of handling noise. We additionally introduce the novel Noisy
Popper ILP system which implements this relaxed approach and is a modification
of the existing Popper system. Like Popper, Noisy Popper takes a
generate-test-constrain loop to search its hypothesis space wherein failed
hypotheses are used to construct hypothesis constraints. These constraints are
used to prune the hypothesis space, making the hypothesis search more
efficient. However, in the relaxed setting, constraints are generated in a more
lax fashion as to avoid allowing noisy training data to lead to hypothesis
constraints which prune optimal hypotheses. Constraints unique to the relaxed
setting are generated via hypothesis comparison. Additional constraints are
generated by weighing the accuracy of hypotheses against their sizes to avoid
overfitting through an application of the minimum description length. We
support this new setting through theoretical proofs as well as experimental
results which suggest that Noisy Popper improves the noise handling
capabilities of Popper but at the cost of overall runtime efficiency.

提出了一种松弛学习法并引入新的 Noisy Popper ILP 系统用于实现，该系统基于 Inductive Logic Programming（ILP）理论，利用松弛策略生成约束条件以避免包含噪声的数据影响回归结果，并在实验中证实，相较于 Popper 系统，Noisy Popper 在噪声数据处理能力上有所提升，但运行效率略有下降。