  Accurate extraction of body text from PDF-formatted academic documents is
essential in text-mining applications for deeper semantic understandings. The
objective is to extract complete sentences in the body text into a txt file
with the original sentence flow and paragraph boundaries. Existing tools for
extracting text from PDF documents would often mix body and nonbody texts. We
devise and implement a system called PDFBoT to detect multiple-column layouts
using a line-sweeping technique, remove nonbody text using computed text
features and syntactic tagging in backward traversal, and align the remaining
text back to sentences and paragraphs. We show that PDFBoT is highly accurate
with average F1 scores of, respectively, 0.99 on extracting sentences, 0.96 on
extracting paragraphs, and 0.98 on removing text on tables, figures, and charts
over a corpus of PDF documents randomly selected from arXiv.org across multiple
academic disciplines.
