This paper tackles the problem of table structure parsing (TSP) from images
in the wild. In contrast to existing studies that mainly focus on parsing
well-aligned tabular images with simple layouts from scanned PDF documents, we
aim to establish a practical table structure parsing system for real-world
scenarios where tabular input images are taken or scanned with severe
deformation, bending or occlusions. For designing such a system, we propose an
approach named Cycle-CenterNet on the top of CenterNet with a novel
cycle-pairing module to simultaneously detect and group tabular cells into
structured tables. In the cycle-pairing module, a new pairing loss function is
proposed for the network training. Alongside with our Cycle-CenterNet, we also
present a large-scale dataset, named Wired Table in the Wild (WTW), which
includes well-annotated structure parsing of multiple style tables in several
scenes like the photo, scanning files, web pages, \emph{etc.}. In experiments,
we demonstrate that our Cycle-CenterNet consistently achieves the best accuracy
of table structure parsing on the new WTW dataset by 24.6\% absolute
improvement evaluated by the TEDS metric. A more comprehensive experimental
analysis also validates the advantages of our proposed methods for the TSP
task.

本研究提出了一种名为 Cycle-CenterNet 的方法，基于新的 cycle-pairing 模块，在解析具有组织结构的表格的同时检测和分组表格单元格，也提供了一个大规模数据集 WTW，用于实验测试表格结构解析方法并在实验中证实了该方法的优势。

从复杂环境中解析表格结构

Parsing Table Structures in the Wild

Important information that relates to a specific topic in a document is often
organized in tabular format to assist readers with information retrieval and
comparison, which may be difficult to provide in natural language. However,
tabular data in unstructured digital documents, e.g., Portable Document Format
(PDF) and images, are difficult to parse into structured machine-readable
format, due to complexity and diversity in their structure and style. To
facilitate image-based table recognition with deep learning, we develop the
largest publicly available table recognition dataset PubTabNet
(this https URL), containing 568k table images with
corresponding structured HTML representation. PubTabNet is automatically
generated by matching the XML and PDF representations of the scientific
articles in PubMed Central Open Access Subset (PMCOA). We also propose a novel
attention-based encoder-dual-decoder (EDD) architecture that converts images of
tables into HTML code. The model has a structure decoder which reconstructs the
table structure and helps the cell decoder to recognize cell content. In
addition, we propose a new Tree-Edit-Distance-based Similarity (TEDS) metric
for table recognition, which more appropriately captures multi-hop cell
misalignment and OCR errors than the pre-established metric. The experiments
demonstrate that the EDD model can accurately recognize complex tables solely
relying on the image representation, outperforming the state-of-the-art by 9.7%
absolute TEDS score.

本研究提出了一个基于编码器 - 解码器结构的深度学习模型，用于将表格图像转换为 HTML 代码，并使用一种新的基于 Tree-Edit-Distance 相似度（TEDS）度量方法实现表格识别，实验结果表明所提出的模型在复杂表格识别方面具有优秀的表现。