# Generating Synthetic Invoices via Layout-Preserving Content Replacement

arXiv: https://arxiv.org/abs/2508.03754 · HTML: https://mlsanity.com/paper/2508.03754/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 4 of 4)

- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [LayoutLM: Pre-training of Text and Layout for Document Image Understanding](https://mlsanity.com/paper/1912.13318/) (2019)
- [LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators](https://mlsanity.com/paper/1901.06767/) (2019)
- [Multi-Content GAN for Few-Shot Font Style Transfer](https://mlsanity.com/paper/1712.00516/) (2017)

## Built on by (most cited first, 1 of 1)

- [Invoice Information Extraction: Methods and Performance Evaluation](https://mlsanity.com/paper/2510.15727/) (2025)

## Abstract (arXiv)

The performance of machine learning models for automated invoice processing is critically dependent on large-scale, diverse datasets. However, the acquisition of such datasets is often constrained by privacy regulations and the high cost of manual annotation. To address this, we present a novel pipeline for generating high-fidelity, synthetic invoice documents and their corresponding structured data. Our method first utilizes Optical Character Recognition (OCR) to extract the text content and precise spatial layout from a source invoice. Select data fields are then replaced with contextually realistic, synthetic content generated by a large language model (LLM). Finally, we employ an inpainting technique to erase the original text from the image and render the new, synthetic text in its place, preserving the exact layout and font characteristics. This process yields a pair of outputs: a visually realistic new invoice image and a perfectly aligned structured data file (JSON) reflecting the synthetic content. Our approach provides a scalable and automated solution to amplify small, private datasets, enabling the creation of large, varied corpora for training more robust and accurate document intelligence models.