Visual Detection with Context for Document Layout Analysis

Conference · Sun Nov 03 04:00:00 UTC 2019

OSTI ID:1558238

Soto, Carlos; Soto, Carlos

We present 1) a work in progress method to visually segment key regions of scientific articles using an object detection technique augmented with contextual features, and 2) a novel dataset of region-labeled articles. A continuing challenge in scientific literature mining is the difficulty of consistently extracting high-quality text from formatted PDFs. To address this, we adapt the object-detection technique Faster R-CNN for document layout detection, and incorporate contextual information that leverages the inherently localized nature of article contents to improve the region detection performance. Due to the limited availability of region-labels for scientific articles, we also contribute a novel dataset of region annotations, the first version of which covers 9 region classes and 822 article pages. Initial experimental results demonstrate a 23.9% absolute improvement in mean average precision over the baseline by incorporating contextual features, and a processing speed 14x faster than a text-based technique. Ongoing work on further improvements is also discussed.

• Research Organization: Brookhaven National Laboratory (BNL), Upton, NY (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (SC-21)
• DOE Contract Number: SC0012704
• OSTI ID: 1558238
• Report Number(s): BNL-212002-2019-COPA
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING