A supervised learning approach for heading detection. Issue 4 (8th January 2020)

• Record Type: Journal Article
• Authors: Budhiraja, Sahib Singh; Mago, Vijay
• Other Names: Saen Reza Farzipoor guestEditor.; Song Malin guestEditor.; Fisher Ron guestEditor.
• Is Part Of: Expert systems. Volume 37:Issue 4(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-01-08
• Subjects: heading detection -- machine learning -- supervised learning algorithm -- text segmentation; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12520
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: As the popularity of the portable document format (PDF) file format increases, research that facilitates PDF text analysis or extraction is necessary. Heading detection is a crucial component of PDF‐based text classification processes. This research involves training a supervised learning model to detect headings by systematically testing and selecting classifier features using recursive feature elimination . Results indicate that decision tree is the best classifier with an accuracy of 95.83%, sensitivity of 0.981, and a specificity of 0.946. This research into heading detection contributes to the field of PDF‐based text extraction and can be applied to the automation of large scale PDF text analysis in a variety of professional and policy‐based contexts.