dc.contributor.author	Kahu, Sampanna Yashwant	en
dc.contributor.committeechair	Fox, Edward A.	en
dc.contributor.committeemember	Diehl, William J.	en
dc.contributor.committeemember	Abbott, A. Lynn	en
dc.contributor.department	Electrical and Computer Engineering	en
dc.date.accessioned	2020-09-30T08:00:25Z	en
dc.date.available	2020-09-30T08:00:25Z	en
dc.date.issued	2020-09-29	en
dc.description.abstract	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).	en
dc.description.abstractgeneral	Portable Document Format (PDF) is one of the most popular document formats. However, parsing PDF files is not a trivial task. One use-case of parsing PDF files is the search functionality on websites hosting scholarly documents (i.e., IEEE Xplore, etc.). Having the ability to extract figures and tables from a scholarly document helps this use-case, among others. Methods using deep learning exist which extract figures from scholarly documents. However, a large number of scholarly documents, especially the ones published before the advent of computers, have been scanned from hard paper copies into PDF. In particular, we focus on scanned PDF versions of long documents, such as Electronic Theses and Dissertations (ETDs). No experiments have been done yet that evaluate the efficacy of the above-mentioned methods on this scanned corpus. This work explores and attempts to improve the performance of these existing methods on scanned ETDs. A new gold standard dataset is created and released as a part of this work for figure extraction from scanned ETDs. Finally, the entire source code and trained model weights are made open-source to aid further research in this field.	en
dc.description.degree	Master of Science	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:27273	en
dc.identifier.uri	http://hdl.handle.net/10919/100113	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Figure Extraction	en
dc.subject	Deep learning (Machine learning)	en
dc.subject	Computer Vision	en
dc.subject	Digital Libraries	en
dc.title	Figure Extraction from Scanned Electronic Theses and Dissertations	en
dc.type	Thesis	en
thesis.degree.discipline	Computer Engineering	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	masters	en
thesis.degree.name	Master of Science	en

Figure Extraction from Scanned Electronic Theses and Dissertations

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