Named Entity Recognition for digitised archival documents in German

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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-12
DOI
https://doi.org/10.5281/zenodo.14419993
Primary Topic
Handwritten Text Recognition Techniques
Type
article
Field-Weighted Citation Impact
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article

Named Entity Recognition for digitised archival documents in German

Zenodo (CERN European Organization for Nuclear Research)
Handwritten Text Recognition Techniques
article

Named Entity Recognition for digitised archival documents in German

article en

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.

Zenodo (CERN European Organization for Nuclear Research)
Karlsruhe Institute of Technology (DE), FIZ Karlsruhe – Leibniz Institute for Information Infrastructure (DE)
Openalex Percentile: Top 67%
Handwritten Text Recognition Techniques
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