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

Automatically extracting key information from scientific documents has the potential to help
scientists work more efficiently and accelerate the pace of scientific progress. Prior work has …

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 …

The unprecedented growth of the research publications in diversified domains has
overwhelmed the research community. It requires a cumbersome process to extract this …

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

Scholarly publishing increasingly requires automated systems that semantically enrich
documents in order to support management and quality assessment of scientific output …

Relevant information extraction is a dire need of the scholarly community. There are a
number of systems available to find relevant information from scientific literature such as …

Purpose. This work addresses an escalating problem within the realm of scientific
publishing, that stems from accelerated publication rates of article formats difficult to process …

Extracting metadata from scholarly papers is an important text mining problem. Widely used
open-source tools such as GROBID are designed for born-digital scholarly papers but often …

In last few decades, with the advent of World Wide Web (WWW), world is being overloaded
with huge data. This huge data carries potential information that once extracted, can be used …