Abstract
Document layout analysis involves understanding the arrangement of elements within a document. This paper navigates the complexities of understanding various elements within document images, such as text, images, tables, and headings. The approach employs an advanced Transformer-based object detection network as an innovative graphical page object detector for identifying tables, figures, and displayed elements. We introduce a query encoding mechanism to provide high-quality object queries for contrastive learning, enhancing efficiency in the decoder phase. We also present a hybrid matching scheme that integrates the decoder's original one-to-one matching strategy with the one-to-many matching strategy during the training phase. This approach aims to improve the model's accuracy and versatility in detecting various graphical elements on a page. Our experiments on PubLayNet, DocLayNet, and PubTables benchmarks show that our approach outperforms current state-of-the-art methods. It achieves an average precision of 97.3% on PubLayNet, 81.6% on DocLayNet, and 98.6 on PubTables, demonstrating its superior performance in layout analysis. These advancements not only enhance the conversion of document images into editable and accessible formats but also streamline information retrieval and data extraction processes.
Abstract (translated)
文档布局分析涉及理解文档中元素的排列。本文探讨了理解文档图像中各种元素（如文本、图片、表格和标题）的复杂性。该方法采用了一种基于Transformer的高级对象检测网络作为创新的图形页面对象检测器，用于识别表格、图中和显示的元素。我们引入了查询编码机制，为对比学习提供高质量的对象查询，提高解码阶段的工作效率。我们还提出了一个混合匹配方案，将解码器的原始一对一匹配策略与训练阶段的一对多匹配策略相结合。这种方法旨在提高模型在检测页面上的各种图形元素方面的准确性和多样性。我们在PubLayNet、DocLayNet和PubTables基准测试上的实验结果表明，我们的方法超越了当前最先进的方法。在PubLayNet上，其平均精度为97.3%；在DocLayNet上，为81.6%；在PubTables上，为98.6%，证明了其在布局分析方面的卓越性能。这些进步不仅使文档图像转换为可编辑和可访问的格式，而且简化了信息检索和数据提取过程。
URL
https://arxiv.org/abs/2404.17888
