Publication
Guided Table Structure Recognition Through Anchor Optimization
Khurram Azeem Hashmi; Didier Stricker; Marcus Liwicki; Muhammad Noman Afzal; Muhammad Zeshan Afzal
In: IEEE Access, Vol. 9, Pages 113521-113534, IEEE, 8/2021.
Abstract
This paper presents the novel approach towards table structure recognition by leveraging the
guided anchors. The concept differs from current state-of-the-art systems 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 using tabular layouts in realistic scenarios. The proposed method is exhaustively evaluated on the
two publicly available datasets of table structure recognition: ICDAR-2013 and TabStructDB. Moreover,
we empirically established the validity of our method by implementing it on the previous approaches.
We accomplished state-of-the-art results on the ICDAR-2013 dataset with an average F1-measure of 94.19%
(92.06% for rows and 96.32% for columns). Thus, a relative error reduction of more than 25% is achieved.
Furthermore, our proposed post-processing improves the average F1-measure to 95.46% that results in a
relative error reduction of more than 35%. Moreover, we surpassed the baseline results on the TabStructDB
dataset with an average F1-measure of 94.57% (94.08% for rows and 95.06% for columns).