Document pair extraction aims to identify key and value entities as well as
their relationships from visually-rich documents. Most existing methods divide
it into two separate tasks: semantic entity recognition (SER) and relation
extraction (RE). However, simply concatenating SER and RE serially can lead to
severe error propagation, and it fails to handle cases like multi-line entities
in real scenarios. To address these issues, this paper introduces a novel
framework, PEneo (Pair Extraction new decoder option), which performs document
pair extraction in a unified pipeline, incorporating three concurrent
sub-tasks: line extraction, line grouping, and entity linking. This approach
alleviates the error accumulation problem and can handle the case of multi-line
entities. Furthermore, to better evaluate the model's performance and to
facilitate future research on pair extraction, we introduce RFUND, a
re-annotated version of the commonly used FUNSD and XFUND datasets, to make
them more accurate and cover realistic situations. Experiments on various
benchmarks demonstrate PEneo's superiority over previous pipelines, boosting
the performance by a large margin (e.g., 19.89%-22.91% F1 score on RFUND-EN)
when combined with various backbones like LiLT and LayoutLMv3, showing its
effectiveness and generality. Codes and the new annotations will be open to the
public.

PEneo 是一种新的文档对提取框架，通过线提取、线分组和实体链接三个并发子任务来解决语义实体识别和关系抽取的错误积累问题和多行实体情况，与 LiLT 和 LayoutLMv3 等网络结构相结合在各种基准测试中表现出良好的性能，提升大幅度（例如，在 RFUND-EN 上的 F1 得分提高了 19.89%-22.91%），效果显著且具有广泛适用性。

PEneo: 将线提取、线分组和实体链接统一为端到端文档对提取

PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for  End-to-end Document Pair Extraction

We present a conceptually simple yet effective algorithm to detect wireframes
in a given image. Compared to the previous methods which first predict an
intermediate heat map and then extract straight lines with heuristic
algorithms, our method is end-to-end trainable and can directly output a
vectorized wireframe that contains semantically meaningful and geometrically
salient junctions and lines. To better understand the quality of the outputs,
we propose a new metric for wireframe evaluation that penalizes overlapped line
segments and incorrect line connectivities. We conduct extensive experiments
and show that our method significantly outperforms the previous
state-of-the-art wireframe and line extraction algorithms. We hope our simple
approach can be served as a baseline for future wireframe parsing studies. Code
has been made publicly available at this https URL

本文提出了一种简单有效的算法来检测给定图像中的线框，并直接输出包含语义方面有意义和几何上突出的交汇点和直线的向量化线框，此方法可以进行端到端的训练，相比以往的方法具有更好的表现。