Purpose–The purpose of this paper is to propose an automatic metadata extraction and
retrieval system to extract bibliographical information from digital academic documents in …

Applications based on scholarly data are of ever increasing importance. This results in
disadvantages for areas where high-quality data and compatible systems are not available …

Within the past few decades we have witnessed digital revolution, which moved scholarly
communication to electronic media and also resulted in a substantial increase in its volume …

Based on state of the art machine learning techniques, GROBID (GeneRation Of
BIbliographic Data) performs reliable bibliographic data extractions from scholar articles …

Extracting information from academic PDF documents is crucial for numerous indexing,
retrieval, and analysis use cases. Choosing the best tool to extract specific content elements …

We introduce a comprehensive end-to-end pipeline designed to extract complete
bibliography section from English scientific articles in digital-born PDF format and further …

This paper proposes OCR++, an open-source framework designed for a variety of
information extraction tasks from scholarly articles including metadata (title, author names …

Bibliographic references containing citation information of academic literature play an
important role as a medium connecting earlier and recent studies. As references contain …

In recent years, we have seen huge volumes of research papers available on the World
Wide Web. Metadata provides a good approach for organizing and retrieving these useful …

Many solutions have been provided to extract bibliographic references from PDF papers.
Machine learning, rule-based and regular expressions approaches were among the most …