A Supervised Learning Approach For Heading Detection

TL;DR

This paper presents a supervised learning method for detecting headings in PDF documents, achieving high accuracy and sensitivity, which enhances automated PDF text analysis for various applications.

Contribution

It introduces a supervised learning approach with feature selection for heading detection in PDFs, demonstrating high accuracy and practical applicability.

Findings

Accuracy of 96.95% in heading detection

Sensitivity of 0.986 indicating high true positive rate

Specificity of 0.953 showing effective true negative detection

Abstract

As the Portable Document Format (PDF) file format increases in popularity, research in analysing its structure for text extraction and analysis is necessary. Detecting headings can be a crucial component of classifying and extracting meaningful data. This research involves training a supervised learning model to detect headings with features carefully selected through recursive feature elimination. The best performing classifier had an accuracy of 96.95%, sensitivity of 0.986 and a specificity of 0.953. 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.