  This paper presents the novel approach towards table structure recognition by
leveraging the guided anchors. The concept differs from current
state-of-the-art approaches for table structure recognition that naively apply
object detection methods. In contrast to prior techniques, first, we estimate
the viable anchors for table structure recognition. Subsequently, these anchors
are exploited to locate the rows and columns in tabular images. Furthermore,
the paper introduces a simple and effective method that improves the results by
using tabular layouts in realistic scenarios. The proposed method is
exhaustively evaluated on the two publicly available datasets of table
structure recognition i.e ICDAR-2013 and TabStructDB. We accomplished
state-of-the-art results on the ICDAR-2013 dataset with an average F-Measure of
95.05$\%$ (94.6$\%$ for rows and 96.32$\%$ for columns) and surpassed the
baseline results on the TabStructDB dataset with an average F-Measure of
94.17$\%$ (94.08$\%$ for rows and 95.06$\%$ for columns).

Computer Vision and Pattern Recognition