Publication Date

2021

Document Type

Thesis

Committee Members

Nikolaos G. Bourbakis, Ph.D. (Advisor); Euripides G.M. Petrakis, Ph.D. (Committee Member); Soon M. Chung, Ph.D. (Committee Member)

Degree Name

Master of Science (MS)

Abstract

A great percentage of documents in scientific and engineering disciplines include mathematical formulas and/or algorithms. Exploring the mathematical formulas in the technical documents, we focused on the mathematical operations associations, their syntactical correctness, and the association of these components into attributed graphs and Stochastic Petri Nets (SPN). We also introduce a formal language to generate mathematical formulas and evaluate their syntactical correctness. The main contribution of this work focuses on the automatic segmentation of mathematical documents for the parsing and analysis of detected algorithmic components. To achieve this, we present a synergy of methods, such as string parsing according to mathematical rules, Formal Language Modeling, optical analysis of technical documents in forms of images, structural analysis of text in images, and graph and Stochastic Petri Net mapping. Finally, for the recognition of the algorithms, we enriched our rule based model with machine learning techniques to acquire better results.

Page Count

127

Department or Program

Department of Computer Science and Engineering

Year Degree Awarded

2021


COinS