Work

Named Entity Recognition for digitised archival documents in German

Year: 2024

Type: conference-paper

Abstract: This paper presents an experiment that evaluates the effectiveness of two different Named Entity Recognition(NER) tools at extracting entities directly from the output of an Optical Character Recognition (OCR) workflow.The authors initially developed a test dataset comprising both raw and corrected OCR outputs, which weremanually annotated with tags for Person, Location, and Organisation. Subsequently, they applied each NER toolto both the raw and corrected OCR outputs, evaluating their performance by comparing the precision, recall, andF1 scores against the manually annotated data.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.14419993

Authors: Vafaie, Mahsa, Sack, Harald, Garay, Nele

Institutions: FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Karlsruhe Institute of Technology

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6893011542

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