metadata

library_name: Spacy
license: mit
tags:
  - Spacy
  - Named entity recognition
metrics:
  - P
  - R
  - F1
language:
  - la
  - de
  - cs
version:
  - Spacy v2

HOME-NACR Named entity recognition

This model detects Person and Location entities in Latin, Czech and German.

Model description

The model has been trained using the Spacy v2 library on the HOME-NACR document annotations. The model is compatible with version 2.3.2 of Spacy and incompatible with versions 3.x.x.

Evaluation results

The model achieves the following results on HOME-NACR:

tag	predicted	matched	Precision	Recall	F1	Support
PERS	28276	28006	0.99	0.997	0.994	28087
LOC	27541	27165	0.986	0.987	0.987	27528
All	55817	55171	0.988	0.992	0.990	55615

How to use

Please refer to the Spacy library page (https://pypi.org/project/spacy/2.3.2/) to use this model.

Cite us!

@inproceedings{10.1007/978-3-031-06555-2_29,
    author = {Monroc, Claire Bizon and Miret, Blanche and Bonhomme, Marie-Laurence and Kermorvant, Christopher},
    title = {A Comprehensive Study Of Open-Source Libraries For Named Entity Recognition On Handwritten Historical Documents},
    year = {2022},
    isbn = {978-3-031-06554-5},
    publisher = {Springer-Verlag},
    address = {Berlin, Heidelberg},
    url = {https://doi.org/10.1007/978-3-031-06555-2_29},
    doi = {10.1007/978-3-031-06555-2_29},
    abstract = {In this paper, we propose an evaluation of several state-of-the-art open-source natural language processing (NLP) libraries for named entity recognition (NER) on handwritten historical documents: spaCy, Stanza and Flair. The comparison is carried out on three low-resource multilingual datasets of handwritten historical documents: HOME (a multilingual corpus of medieval charters), Balsac (a corpus of parish records from Quebec), and Esposalles (a corpus of marriage records in Catalan). We study the impact of the document recognition processes (text line detection and handwriting recognition) on the performance of the NER. We show that current off-the-shelf NER libraries yield state-of-the-art results, even on low-resource languages or multilingual documents using multilingual models. We show, in an end-to-end evaluation, that text line detection errors have a greater impact than handwriting recognition errors. Finally, we also report state-of-the-art results on the public Esposalles dataset.},
    booktitle = {Document Analysis Systems: 15th IAPR International Workshop, DAS 2022, La Rochelle, France, May 22–25, 2022, Proceedings},
    pages = {429–444},
    numpages = {16},
    keywords = {Text line detection, Named entity recognition, Handwritten historical documents},
    location = {La Rochelle, France}
}