Augraphy: A Data Augmentation Library for Document Images
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This paper introduces Augraphy, a Python package geared toward realistic data augmentation strategies for document images. Augraphy uses many different augmentation strategies to produce augmented versions of clean document images that appear as if they have been distorted from standard office operations, such as printing, scanning, and faxing through old or dirty machines, degradation of ink over time, and handwritten markings. Augraphy can be used both as a data augmentation tool for (1) producing diverse training data for tasks such as document de-noising, and (2) generating challenging test data for evaluating model robustness on document image modeling tasks. This paper provides an overview of Augraphy and presents three example robustness testing use-cases of Augraphy.
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