A Two-Step Approach for Automatic OCR Post-Correction

1 code implementation • COLING (LaTeCHCLfL, CLFL, LaTeCH) 2020 • Robin Schaefer, Clemens Neudecker

The quality of Optical Character Recognition (OCR) is a key factor in the digitisation of historical documents.

Optical Character Recognition Optical Character Recognition (OCR) +1

QURATOR: Innovative Technologies for Content and Data Curation

no code implementations • 25 Apr 2020 • Georg Rehm, Peter Bourgonje, Stefanie Hegele, Florian Kintzel, Julián Moreno Schneider, Malte Ostendorff, Karolina Zaczynska, Armin Berger, Stefan Grill, Sören Räuchle, Jens Rauenbusch, Lisa Rutenburg, André Schmidt, Mikka Wild, Henry Hoffmann, Julian Fink, Sarah Schulz, Jurica Seva, Joachim Quantz, Joachim Böttger, Josefine Matthey, Rolf Fricke, Jan Thomsen, Adrian Paschke, Jamal Al Qundus, Thomas Hoppe, Naouel Karam, Frauke Weichhardt, Christian Fillies, Clemens Neudecker, Mike Gerber, Kai Labusch, Vahid Rezanezhad, Robin Schaefer, David Zellhöfer, Daniel Siewert, Patrick Bunk, Lydia Pintscher, Elena Aleynikova, Franziska Heine

In all domains and sectors, the demand for intelligent systems to support the processing and generation of digital content is rapidly increasing.

An Open Corpus for Named Entity Recognition in Historic Newspapers

1 code implementation • LREC 2016 • Clemens Neudecker

The availability of openly available textual datasets ({``}corpora{''}) with highly accurate manual annotations ({``}gold standard{''}) of named entities (e. g. persons, locations, organizations, etc.)

named-entity-recognition Named Entity Recognition +1