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.

这篇论文探讨了文档布局分析，使用先进的基于 Transformer 的对象检测网络作为创新的图形页面对象检测器，以识别表格、图形和展示元素。通过引入查询编码机制，提供高质量的对象查询进行对比学习，增强了解码器阶段的效率。同时，本方法采用混合匹配方案，在训练阶段将解码器的一对一匹配策略与一对多匹配策略相结合，旨在提高模型在页面上检测各种图形元素的准确性和通用性。实验结果表明，该方法在 PubLayNet、DocLayNet 和 PubTables 等基准测试上的表现超过了现有的最先进方法，PubLayNet 的平均精度为 97.3%，DocLayNet 的精度为 81.6%，PubTables 的精度为 98.6%，证明了其在布局分析方面的优越性能。这些进步不仅提高了将文档图片转换为可编辑和可访问格式的能力，还简化了信息检索和数据提取过程。

文档图像中的文档布局分析的混合方法

A Hybrid Approach for Document Layout Analysis in Document images

Automatic text summarization has experienced substantial progress in recent
years. With this progress, the question has arisen whether the types of
summaries that are typically generated by automatic summarization models align
with users' needs. Ter Hoeve et al (2020) answer this question negatively.
Amongst others, they recommend focusing on generating summaries with more
graphical elements. This is in line with what we know from the
psycholinguistics literature about how humans process text. Motivated from
these two angles, we propose a new task: summarization with graphical elements,
and we verify that these summaries are helpful for a critical mass of people.
We collect a high quality human labeled dataset to support research into the
task. We present a number of baseline methods that show that the task is
interesting and challenging. Hence, with this work we hope to inspire a new
line of research within the automatic summarization community.

自动文本摘要在近年来取得了实质性的进展。Ter Hoeve 等认为自动摘要生成的类型与用户需求不符，推荐增加更多图形元素。基于这个任务，我们提出了摘要与图形元素结合的新任务，并验证这些摘要对大众有助益。我们收集了一个高质量的人工标注数据集，提供基线模型表明该任务是有趣和具有挑战性的。希望这项工作能够在自动摘要社区中引起新的研究线路。