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 # Augraphy: Creating Realistic Document Image Datasets with Data Augmentation
 
-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.
+This paper introduces [*Augraphy*](https://github.com/sparkfish/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.
 
 **Why _Augraphy_**