2024-09-13: Paper Summary: Uncertainty Quantification in Table Structure Recognition
Figure 1. An illustration of the differences between aleatoric and epistemic uncertainties (Yang et al., 2023). Introduction Table Structure Recognition (TSR) is a task of document analysis that focuses on identifying rows and columns in digital table images [4]. While current TSR methods can identify cell locations, they lack the ability to predict uncertainties in their results [1]. This limitation has hindered the real-world application of TSR, such as automatically extracting data from table images in physical sciences. In this blog post, we summarize our paper titled " Uncertainty Quantification (UQ) for Table Structure Recognition ", presented at the 2024 IEEE International Conference on Information Reuse and Integration for Data Science . In this paper, we proposed a method called TTA-m (Test-Time Augmentation with multiple models) that aims to quantify uncertainties in TSR predictions, potentially enhancing ho...