Vital Records: Uncover the past from historical handwritten records

Herve Dejean, Jean-Luc Meunier


Abstract
We present Vital Records, a demonstrator based on deep-learning approaches to handwritten-text recognition, table processing and information extraction, which enables data from century-old documents to be parsed and analysed, making it possible to explore death records in space and time. This demonstrator provides a user interface for browsing and visualising data extracted from 80,000 handwritten pages of tabular data.
Anthology ID:
2020.latechclfl-1.8
Volume:
Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature
Month:
December
Year:
2020
Address:
Online
Venues:
CLFL | COLING | LaTeCH | LaTeCHCLfL
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
69–73
Language:
URL:
https://aclanthology.org/2020.latechclfl-1.8
DOI:
Bibkey:
Cite (ACL):
Herve Dejean and Jean-Luc Meunier. 2020. Vital Records: Uncover the past from historical handwritten records. In Proceedings of the The 4th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pages 69–73, Online. International Committee on Computational Linguistics.
Cite (Informal):
Vital Records: Uncover the past from historical handwritten records (Dejean & Meunier, LaTeCHCLfL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.latechclfl-1.8.pdf