Web Science and Digital Libraries Research Group

                   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...

Document Understanding  is the task of automatically parsing and ingesting the content of documents into a system using artificial intelligence methods to accomplish downstream challenges, such as information retrieval, Q&A, text and non-textual analysis. Document Understanding  has trending importance in processing digital documents at scale. Many documents are visually rich, meaning layout and visual information are critical to understanding document content. In the scholarly domain, the layout analysis is challenging due to various document templates (e.g., single or double-column papers), which have title pages, section headings, tables, figures, algorithms, equations, references, and so on. To build an intelligent system to process such downstream tasks, annotating a large number of documents is laborious. Besides, developing training data with an equal amount of samples for each template is challenging and may not be attainable at a large scale. Thus, we often see im...