Uncertainty Quantification in Table Structure Recognition

TL;DR

This paper introduces a novel uncertainty quantification method for table structure recognition using a mixture-of-experts approach, enhancing model reliability by identifying low-confidence predictions through innovative heuristics.

Contribution

It presents the first method for uncertainty quantification in TSR, utilizing test-time augmentation and heuristics to detect uncertain cells, improving model interpretability and accuracy.

Findings

Effective uncertainty quantification demonstrated on benchmark datasets.

Heuristics successfully differentiate uncertain cells from normal cells.

Method reduces human verification effort in TSR applications.

Abstract

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.