PageAug

PageAug is a technique for data augmentation in the context of document analysis and natural language processing. It is designed to artificially increase the size and diversity of a training dataset by generating modified versions of existing documents. The core idea behind PageAug is to simulate common variations that can occur in real-world documents without altering the fundamental semantic content.

The augmentation process typically involves applying a series of transformations to the original document pages. These transformations can include adjustments to layout, such as changing margins, adding or removing white space, or slightly shifting text blocks. PageAug may also introduce visual noise, alter font styles or sizes, and modify the brightness or contrast of scanned documents. The goal is to create synthetic data that mimics the challenges of real-world document variability, which can improve the robustness and generalization capabilities of machine learning models trained on such data.

By exposing models to these augmented examples, PageAug aims to enhance their performance on tasks like document classification, information extraction, and optical character recognition (OCR). This is particularly useful when dealing with datasets that are small or lack the diversity needed for effective model training. The generated data retains the original text while introducing realistic visual and structural perturbations, allowing models to learn to be less sensitive to minor variations in document appearance.