Table Structure Extraction With Bi-Directional Gated Recurrent Unit Networks

Saqib Ali Khan, Syed Muhammad Daniyal Khalid, Muhammad Ali Shahzad, Faisal Shafait

Abstract:

Tables present summarized and structured infor-
mation to the reader, which makes table’s 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 softmax 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