Automatic Deep Understanding of Tables in Technical Documents
Michail S. Alexiou, Nikolaos Bourbakis · 2020
In order to achieve a deep understanding of the information presented in technical documents, one must first understand the document's individual modalities, such as tables, graphics, diagrams, etc. Accurate detection and recognition of these individual modalities constitute key processing steps to ensure efficient understanding as well. In this paper, we present a methodology for understanding the deeper associations of tabular information and expressing them in stochastic Petri-net graphs. This research is focused on tables that follow strictly the IEEE format rules. We divide this work into three distinct steps 1) table detection, 2) table recognition, 3) table understanding. For the part of detection, we study different machine learning and rule-based methodologies for classifying images as tables and compare the results from their evaluation. During the table recognition step, we extract all the necessary information from the table and recognize associations between them. We then convert the recognized associations into attributed graphs, natural language text, and SPN graphs. Finally, we present the preliminary results from the conversion of tabular information into the other forms of information representation.