Figure Extraction from Scanned Electronic Theses and Dissertations

The ability to extract figures and tables from scientific documents can solve key use-cases such as their semantic parsing, summarization, or indexing. Although a few methods have been developed to extract figures and tables from scientific documents, their performance on scanned counterparts is considerably lower than on born-digital ones. To facilitate this, we propose methods to effectively extract figures and tables from Electronic Theses and Dissertations (ETDs), that out-perform existing methods by a considerable margin. Our contribution towards this goal is three-fold. (a) We propose a system/model for improving the performance of existing methods on scanned scientific documents for figure and table extraction. (b) We release a new dataset containing 10,182 labelled page-images spanning across 70 scanned ETDs with 3.3k manually annotated bounding boxes for figures and tables. (c) Lastly, we release our entire code and the trained model weights to enable further research (https://github.com/SampannaKahu/deepfigures-open).

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Computer Engineering

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2020

Author dc:creator

• Kahu, Sampanna Yashwant

Chair dc:contributor.committeechair

• Fox, Edward A.

Committee members dc:contributor.committeemember

• Diehl, William J.
• Abbott, A. Lynn

• Figure Extraction
• Deep learning (Machine learning)
• Computer Vision
• Digital Libraries

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Dc Identifier Other

vt_gsexam:27273

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/100113