Scientific documents contain tables that list important information in a
concise fashion. Structure and content extraction from tables embedded within
PDF research documents is a very challenging task due to the existence of
visual features like spanning cells and content features like mathematical
symbols and equations. Most existing table structure identification methods
tend to ignore these academic writing features. In this paper, we adapt the
transformer-based language modeling paradigm for scientific table structure and
content extraction. Specifically, the proposed model converts a tabular image
to its corresponding LaTeX source code. Overall, we outperform the current
state-of-the-art baselines and achieve an exact match accuracy of 70.35 and
49.69% on table structure and content extraction, respectively. Further
analysis demonstrates that the proposed models efficiently identify the number
of rows and columns, the alphanumeric characters, the LaTeX tokens, and
symbols.

本文提供了一种基于 Transformer 的科学表格结构和内容提取方法，能够解决 PDF 研究文档中存在的跨度单元格和公式等复杂因素，最终精确匹配表格结构和内容，并高效识别表格的各种特征。

科学表格格式和内容提取到 LaTeX

Tables to LaTeX: structure and content extraction from scientific tables

Tables present summarized and structured information to the reader, which
makes table structure extraction an important part of document understanding
applications. However, table structure identification is a hard problem not
only because of the large variation in the table layouts and styles, but also
owing to the variations in the page layouts and the noise contamination levels.
A lot of research has been done to identify table structure, most of which is
based on applying heuristics with the aid of optical character recognition
(OCR) to hand pick layout features of the tables. These methods fail to
generalize well because of the variations in the table layouts and the errors
generated by OCR. In this paper, we have proposed a robust deep learning based
approach to extract rows and columns from a detected table in document images
with a high precision. In the proposed solution, the table images are first
pre-processed and then fed to a bi-directional Recurrent Neural Network with
Gated Recurrent Units (GRU) followed by a fully-connected layer with soft max
activation. The network scans the images from top-to-bottom as well as
left-to-right and classifies each input as either a row-separator or a
column-separator. We have benchmarked our system on publicly available UNLV as
well as ICDAR 2013 datasets on which it outperformed the state-of-the-art table
structure extraction systems by a significant margin.

本文提出了一种基于深度学习的方法，它首先对表格图像进行预处理，然后将其输入到具有门控循环单元（GRU）的双向循环神经网络中，最终将结果分类为行分隔符或列分隔符。该方法在表格结构提取方面取得了显著的性能提升。