论文标题
Augraphy：文档图像的数据增强库
Augraphy: A Data Augmentation Library for Document Images
论文作者
论文摘要
本文介绍了Augraphy，这是一个用于构建数据增强管道的Python库，这些库会产生在现实世界文档图像数据集中常见的扭曲。 Augraphy除了提供许多不同的策略来产生增强版本的清洁文档图像，这些策略看起来像是通过标准办公室操作所改变的，例如打印，扫描和通过旧机器或肮脏的机器，墨水降低，随着时间的推移和手写标记，它们似乎已被标准的办公室操作（例如打印，扫描和传真），以及诸如标准办公室操作（例如打印，扫描和传真），以及。本文讨论了Augraphy工具，并展示了如何将其用作数据增强工具，用于为文档DeNoising等任务生成多样化的培训数据，并用于生成具有挑战性的测试数据以评估文档图像建模任务上的模型鲁棒性。
This paper introduces Augraphy, a Python library for constructing data augmentation pipelines which produce distortions commonly seen in real-world document image datasets. Augraphy stands apart from other data augmentation tools by providing many different strategies to produce augmented versions of clean document images that appear as if they have been altered by standard office operations, such as printing, scanning, and faxing through old or dirty machines, degradation of ink over time, and handwritten markings. This paper discusses the Augraphy tool, and shows how it can be used both as a data augmentation tool for producing diverse training data for tasks such as document denoising, and also for generating challenging test data to evaluate model robustness on document image modeling tasks.
