文档AI：基于Transformer、图模型和卷积神经网络的文档布局分析比较研究
Document AI: A Comparative Study of Transformer-Based, Graph-Based Models, and Convolutional Neural Networks For Document Layout Analysis
AI总结：
本研究比较评估了基于Transformer、图和CNN的文档布局分析模型，并利用机器翻译探索跨语言知识迁移的潜力，以填补先前缺乏系统比较的空白。
摘要：
文档AI旨在通过利用自然语言处理和计算机视觉技术自动分析文档。文档AI的主要任务之一是文档布局分析，该任务通过解释布局、图像和文本的内容及空间关系来结构化文档页面。此任务可以是图像中心的，即目标是识别和标注诸如作者和段落等不同区域；也可以是文本中心的，即重点是分类文档中的单个单词。尽管用于改进布局分析的方法日益复杂，但关于其发现能在多大程度上推广到更广泛背景仍存在疑问。具体而言，先前的工作开发了基于非常不同架构的系统，如基于Transformer的、基于图的以及CNN。然而，没有工作提及这些模型在比较分析中的有效性。此外，尽管已经开发了能够进行知识迁移的语言无关文档AI模型，但它们能有效迁移知识的程度仍有待研究。在本研究中，我们旨在通过进行文档布局分析中最先进模型的比较评估，并利用机器翻译技术探索跨语言布局分析的潜力，来填补这些空白。
英文摘要：
Document AI aims to automatically analyze documents by leveraging natural language processing and computer vision techniques. One of the major tasks of Document AI is document layout analysis, which structures document pages by interpreting the content and spatial relationships of layout, image, and text. This task can be image-centric, wherein the aim is to identify and label various regions such as authors and paragraphs, or text-centric, where the focus is on classifying individual words in a document. Although there are increasingly sophisticated methods for improving layout analysis, doubts remain about the extent to which their findings can be generalized to a broader context. Specifically, prior work developed systems based on very different architectures, such as transformer-based, graph-based, and CNNs. However, no work has mentioned the effectiveness of these models in a comparative analysis. Moreover, while language-independent Document AI models capable of knowledge transfer have been developed, it remains to be investigated to what degree they can effectively transfer knowledge. In this study, we aim to fill these gaps by conducting a comparative evaluation of state-of-the-art models in document layout analysis and investigating the potential of cross-lingual layout analysis by utilizing machine translation techniques.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
