Deep Understanding of Technical Documents: An Enhancement on Diagrams Understanding

Author:

Alexiou Michail S.1,Gkorgkolis Nikolaos1,Mertoguno Sukarno2,Bourbakis Nikolaos G.1

Affiliation:

1. CART Center, Wright State University, Dayton, Ohio, USA

2. Georgia Tech, GTRI, Atlanta, Georgia

Abstract

Humans are capable of understanding the knowledge that is included in technical documents automatically by consciously combining the information that is presented in the document’s individual modalities. These modalities are mathematical formulas, charts, tables, diagram images and etc. In this paper, we significantly enhance a previously presented technical document understanding methodology3 that emulates the way that humans also perceive information. More specifically, we make the original diagram understanding methodology adaptive to larger architectures with more complex structures and modules. The overall understanding methodology results in the generation of a Stochastic Petri-net (SPN) graph that describes the system’s functionality. Finally, we conclude with the introduction of the hierarchical association of different diagram images from the same technical document. This processing step aims to provide a holistic understanding of all illustrated diagram information.

Publisher

World Scientific Pub Co Pte Lt

Subject

Artificial Intelligence,Artificial Intelligence

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Abax: Extracting Mathematical Formulas from Chart Images Using Spatial Pixel Information;International Journal on Artificial Intelligence Tools;2024-03

2. Behavioral analysis of bar charts in documents via stochastic petri-net modeling;Pattern Recognition Letters;2023-12

3. An Evaluation of Table Detection Methods in Document Images;2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI);2023-11-06

4. Extracting Pseudocode from Digital Block Diagram in Technical Documents;International Journal on Artificial Intelligence Tools;2023-09

5. Pinakas: A Methodology for Deep Analysis of Tables in Technical Documents;International Journal on Artificial Intelligence Tools;2023-06

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