# Augraphy: A Data Augmentation Library for Document Images

arXiv: https://arxiv.org/abs/2208.14558 · HTML: https://mlsanity.com/paper/2208.14558/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 50 references that are in the map.
- First posted on arXiv 30 Aug 2022.

## Builds on (most cited first, 22 of 22)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Simple Baselines for Image Restoration](https://mlsanity.com/paper/2204.04676/) (2022)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents](https://mlsanity.com/paper/1905.13538/) (2019)
- [Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval](https://mlsanity.com/paper/1502.07058/) (2015)
- [fastai: A Layered API for Deep Learning](https://mlsanity.com/paper/2002.04688/) (2020)
- [Data Augmentation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1705.00440/) (2017)
- [Vision Transformer for Small-Size Datasets](https://mlsanity.com/paper/2112.13492/) (2021)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [Examining the Impact of Blur on Recognition by Convolutional Networks](https://mlsanity.com/paper/1611.05760/) (2016)
- [Google's Cloud Vision API Is Not Robust To Noise](https://mlsanity.com/paper/1704.05051/) (2017)
- [AugLy: Data Augmentations for Robustness](https://mlsanity.com/paper/2201.06494/) (2022)
- [MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream](https://mlsanity.com/paper/1807.05786/) (2018)
- [Training Vision Transformers with Only 2040 Images](https://mlsanity.com/paper/2201.10728/) (2022)
- [How Image Degradations Affect Deep CNN-based Face Recognition?](https://mlsanity.com/paper/1608.05246/) (2016)
- [The Newspaper Navigator Dataset: Extracting And Analyzing Visual Content from 16 Million Historic Newspaper Pages in Chronicling America](https://mlsanity.com/paper/2005.01583/) (2020)
- [End-to-End Unsupervised Document Image Blind Denoising](https://mlsanity.com/paper/2105.09437/) (2021)
- [Detection Masking for Improved OCR on Noisy Documents](https://mlsanity.com/paper/2205.08257/) (2022)
- [Audiogmenter: a MATLAB Toolbox for Audio Data Augmentation](https://mlsanity.com/paper/1912.05472/) (2019)

## Built on by (most cited first, 14 of 14)

- [Evaluating Out-of-Distribution Performance on Document Image Classifiers](https://mlsanity.com/paper/2210.07448/) (2022)
- [Beyond Document Page Classification: Design, Datasets, and Challenges](https://mlsanity.com/paper/2308.12896/) (2023)
- [CompCap: Improving Multimodal Large Language Models with Composite Captions](https://mlsanity.com/paper/2412.05243/) (2024)
- [Qianfan-VL: Domain-Enhanced Universal Vision-Language Models](https://mlsanity.com/paper/2509.18189/) (2025)
- [On Web-based Visual Corpus Construction for Visual Document Understanding](https://mlsanity.com/paper/2211.03256/) (2022)
- [DocStormer: Revitalizing Multi-Degraded Colored Document Images to Pristine PDF](https://mlsanity.com/paper/2310.17910/) (2023)
- [AdaParse: An Adaptive Parallel PDF Parsing and Resource Scaling Engine](https://mlsanity.com/paper/2505.01435/) (2025)
- [Enhancing Document Key Information Localization Through Data Augmentation](https://mlsanity.com/paper/2502.06132/) (2025)
- [Typhoon OCR: Open Vision-Language Model For Thai Document Extraction](https://mlsanity.com/paper/2601.14722/) (2026)
- [Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training](https://mlsanity.com/paper/2605.10835/) (2026)
- [Synth-JDoc: Synthesizing a Japanese Document Image Dataset for OCR with Diverse Layouts and Embedded Images](https://mlsanity.com/paper/2608.28248/) (2026)
- [Full-Page Optical Music Recognition of Handwritten Monophonic Scores](https://mlsanity.com/paper/2609.05662/) (2026)
- [WeVisDoc: From Coverage to Capability for Robust End-to-End Document Parsing](https://mlsanity.com/paper/2609.20423/) (2026)
- [Evaluating In-Context Learning and Retrieval Strategies for Devanagari Post-OCR Correction](https://mlsanity.com/paper/2609.21595/) (2026)

## Abstract (arXiv)

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.