Survey and empirical comparison of different approaches for text extraction from scholarly figures

Survey and empirical comparison of different approaches for text extraction from scholarly figures Different approaches have been proposed in the past to address the challenge of extracting text from scholarly figures. However, until recently, no comparative evaluation of the different approaches had been conducted. Thus, we performed an extensive study of the related work and evaluated in total 32 different approaches. In this work, we perform a more detailed comparison of the 7 most relevant approaches described in the literature and extend to 37 systematic linear combinations of methods for extracting text from scholarly figures. Our generic pipeline, consisting of six steps, allows us to freely combine the different possible methods and perform a fair comparison. Overall, we have evaluated 44 different linear pipeline configurations and systematically compared the different methods. We then derived two non-linear configurations and a two-pass approach. We evaluate all pipeline configurations over four datasets of scholarly figures of different origin and characteristics. The quality of the extraction results is assessed using F-measure and Levenshtein distance, and we measure the runtime performance. Our experiments showed that there is a linear configuration that overall shows the best text extraction quality on all datasets. Further experiments showed that the best configuration can be improved by extending it to a two-pass approach. Regarding the runtime, we observed huge differences from very fast approaches to those running for several weeks. Our experiments found the best working configuration for text extraction from our method set. However, they also showed that further improvements regarding region extraction and classification are needed. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Survey and empirical comparison of different approaches for text extraction from scholarly figures

Loading next page...
 
/lp/springer_journal/survey-and-empirical-comparison-of-different-approaches-for-text-AIDvMCIyWH
Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-018-6162-7
Publisher site
See Article on Publisher Site

Abstract

Different approaches have been proposed in the past to address the challenge of extracting text from scholarly figures. However, until recently, no comparative evaluation of the different approaches had been conducted. Thus, we performed an extensive study of the related work and evaluated in total 32 different approaches. In this work, we perform a more detailed comparison of the 7 most relevant approaches described in the literature and extend to 37 systematic linear combinations of methods for extracting text from scholarly figures. Our generic pipeline, consisting of six steps, allows us to freely combine the different possible methods and perform a fair comparison. Overall, we have evaluated 44 different linear pipeline configurations and systematically compared the different methods. We then derived two non-linear configurations and a two-pass approach. We evaluate all pipeline configurations over four datasets of scholarly figures of different origin and characteristics. The quality of the extraction results is assessed using F-measure and Levenshtein distance, and we measure the runtime performance. Our experiments showed that there is a linear configuration that overall shows the best text extraction quality on all datasets. Further experiments showed that the best configuration can be improved by extending it to a two-pass approach. Regarding the runtime, we observed huge differences from very fast approaches to those running for several weeks. Our experiments found the best working configuration for text extraction from our method set. However, they also showed that further improvements regarding region extraction and classification are needed.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Jun 2, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off