Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Table Structure Extraction with Bi-directional Gated Recurrent Unit
  Networks   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.

24
The categories of this paper are: dg-ga
5
The categories of this paper are: gr-qc
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess