Abstract
Document layout understanding is a field of study that analyzes the spatial arrangement of information in a document hoping to understand its structure and layout. Models such as LayoutLM (and its subsequent iterations) can understand semi-structured documents with SotA results; however, the lack of open semi-structured data is a limitation in itself. While semi-structured data is common in everyday life (balance sheets, purchase orders, receipts), there is a lack of public datasets for training machine learning models for this type of document. In this investigation we propose a method to generate new, synthetic, layout information that can help overcoming this data shortage. According to our results, the proposed method performs better than LayoutTransformer, another popular layout generation method. We also show that, in some scenarios, text classification can improve when supported by bounding box information.
Abstract (translated)
文档布局理解是一个研究领域，它分析文档中信息的空间排列，旨在了解其结构和布局。像LayoutLM及其后续版本这样的模型可以以最先进的结果来理解和处理半结构化文档；然而，缺乏公开的半结构化数据本身就是一个限制。尽管在日常生活中，半结构化的数据非常普遍（如资产负债表、采购订单、收据），但用于训练此类文档机器学习模型的公共数据集却很匮乏。在这项研究中，我们提出了一种生成新的合成布局信息的方法，以帮助克服这一数据短缺问题。根据我们的结果，所提出的方法优于另一种流行的布局生成方法LayoutTransformer。此外，我们还表明，在某些场景下，文本分类在有边界框信息支持的情况下可以得到改进。
URL
https://arxiv.org/abs/2501.05497
