  Quantifying uncertainties for machine learning models is a critical step to
reduce human verification effort by detecting predictions with low confidence.
This paper proposes a method for uncertainty quantification (UQ) of table
structure recognition (TSR). The proposed UQ method is built upon a
mixture-of-expert approach termed Test-Time Augmentation (TTA). Our key idea is
to enrich and diversify the table representations, to spotlight the cells with
high recognition uncertainties. To evaluate the effectiveness, we proposed two
heuristics to differentiate highly uncertain cells from normal cells, namely,
masking and cell complexity quantification. Masking involves varying the pixel
intensity to deem the detection uncertainty. Cell complexity quantification
gauges the uncertainty of each cell by its topological relation with
neighboring cells. The evaluation results based on standard benchmark datasets
demonstrate that the proposed method is effective in quantifying uncertainty in
TSR models. To our best knowledge, this study is the first of its kind to
enable UQ in TSR tasks. Our code and data are available at:
https://github.com/lamps-lab/UQTTA.git.
