  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.
