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.
Abstract (translated)
机器学习模型在自动化发票处理中的性能严重依赖于大规模、多样化的数据集。然而，由于隐私法规的限制和人工标注高昂的成本，获取这样的数据集变得非常困难。为了解决这个问题，我们提出了一种新颖的数据生成流程，用于创建高保真度的合成发票文档及其对应的结构化数据。 我们的方法首先利用光学字符识别（OCR）技术从原始发票中提取文本内容和精确的空间布局信息。然后，选择性地用大型语言模型（LLM）生成的情境真实性的合成内容替换部分数据字段。最后，我们使用一种图像修复技术去除原图中的文字，并在相同的位置渲染新的合成文字，保留了原有的版式和字体特征。 这一过程会产生一对输出：一张视觉上逼真的新发票图片以及一个与其合成内容完全对齐的结构化数据文件（JSON）。我们的方法提供了一种可扩展且自动化的解决方案来放大小规模、私有的数据集，从而能够创建大规模、多样化的语料库，用于训练更加强大和准确的文档智能模型。
URL
https://arxiv.org/abs/2508.03754
