TabAug: Data Driven Augmentation for Enhanced Table Structure Recognition

Umar Khan, Sohaib Zahid, Muhammad Asad Ali, Adnan Ul-Hasan, Faisal Shafait

Abstract:

Table Structure Recognition is an essential part of end-to-
end tabular data extraction in document images. The recent success
of deep learning model architectures in computer vision remains to be
non-reflective in table structure recognition, largely because extensive
datasets for this domain are still unavailable while annotating new data
is expensive and time-consuming. Traditionally, in computer vision, these
challenges are addressed by standard augmentation techniques that are
based on image transformations like color jittering and random crop-
ping. As demonstrated by our experiments, these techniques are not
effective for the task of table structure recognition. In this paper, we
propose TabAug, a re-imagined Data Augmentation technique that pro-
duces structural changes in table images through replication and deletion
of rows and columns. It also consists of a data-driven probabilistic model
that allows control over the augmentation process. To demonstrate the
efficacy of our approach, we perform experimentation on ICDAR 2013
dataset where our approach shows consistent improvements in all aspects
of the evaluation metrics, with cell-level correct detections improving
from 92.16% to 96.11% over the baseline.