A simple and effective figure caption detection system for old-style documents

Zongyi Liu; Hanning Zhou

doi:10.1117/12.872144

24 January 2011 A simple and effective figure caption detection system for old-style documents

Zongyi Liu, Hanning Zhou

Proceedings Volume 7874, Document Recognition and Retrieval XVIII; 78740T (2011) https://doi.org/10.1117/12.872144
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

Abstract

Identifying figure captions has wide applications in producing high quality e-books such as kindle books or ipad books. In this paper, we present a rule-based system to detect horizontal figure captions in old-style documents. Our algorithm consists of three steps: (i) segment images into regions of different types such as text and figures, (ii) search the best caption region candidate based on heuristic rules such as region alignments and distances, and (iii) expand caption regions identified in step (ii) with its neighboring text-regions in order to correct oversegmentation errors. We test our algorithm using 81 images collected from old-style books, with each image containing at least one figure area. We show that the approach is able to correctly detect figure captions from images with different layouts, and we also measure its performances in terms of both precision rate and recall rate.

Citation Download Citation

Zongyi Liu and Hanning Zhou "A simple and effective figure caption detection system for old-style documents", Proc. SPIE 7874, Document Recognition and Retrieval XVIII, 78740T (24 January 2011); https://doi.org/10.1117/12.872144

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available