  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 labeling 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 cropping. 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
produces 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.
