🤖 AI Summary
To address the challenge of acquiring invoice data under stringent privacy regulations and high manual annotation costs, this paper proposes an end-to-end synthetic data generation framework. First, OCR precisely extracts textual content and spatial layout from real invoices. Second, a large language model generates semantically coherent and format-compliant replacement text. Third, image inpainting techniques render the new text at original positions with consistent font, size, and styling, yielding photorealistic, layout-aligned synthetic invoice images alongside corresponding JSON-structured annotations. This is the first method to jointly preserve layout fidelity, typographic consistency, and semantic validity in synthetic invoice generation. By eliminating reliance on sensitive real-world data, it enables scalable, diverse, and fully annotated invoice dataset augmentation—thereby significantly improving the robustness and accuracy of document intelligence models.
📝 Abstract
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.