Summary of the paper

Title DeepPDF: A Deep Learning Approach to Extracting Text from PDFs
Authors Christopher Stahl, Steven Young, Drahomira Herrmannova, Robert Patton, Jack Wells
Abstract Scientific publications contain a plethora of important information, not only for researchers but also for their managers and institutions. Many researchers try to collect and extract this information in large enough quantities that it requires machine automation, but because publications were historically intended for print and not machine consumption, the digital document formats used today (primarily PDF) have created many hurdles for text extraction. Primarily, tools have relied on trying to convert PDF's to plain text documents for machine processing by reverse engineering the PDF standard. A complex process because once a PDF is created it is more closely related to an image file than a document markup language. In this paper we explore the feasibility of treating these PDF documents as images as opposed to a proprietary markup language. We believe that by using deep learning and image analysis we can create more accurate PDF to text extraction tools than those that currently exist. \\ \newline \Keywords{deep learning, text extraction, information extraction, PDF extraction, scholarly publications.
Full paper DeepPDF: A Deep Learning Approach to Extracting Text from PDFs
Bibtex @InProceedings{STAHL18.14,
  author = {Christopher Stahl ,Steven Young ,Drahomira Herrmannova ,Robert Patton and Jack Wells},
  title = {DeepPDF: A Deep Learning Approach to Extracting Text from PDFs},
  booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
  year = {2018},
  month = {may},
  date = {7-12},
  location = {Miyazaki, Japan},
  editor = {},
  publisher = {European Language Resources Association (ELRA)},
  address = {Paris, France},
  isbn = {979-10-95546-20-7},
  language = {english}
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