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。