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Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers

Barman, Raphaël; Ehrmann, Maud; Clematide, Simon; Oliveira, Sofia Ares; Kaplan, Frédéric (2021). Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers. Journal of Data Mining in Genomics & Proteomics:online.

Abstract

The massive amounts of digitized historical documents acquired over the last decades naturally lend themselves to automatic processing and exploration. Research work seeking to automatically process facsimiles and extract information thereby are multiplying with, as a first essential step, document layout analysis. If the identification and categorization of segments of interest in document images have seen significant progress over the last years thanks to deep learning techniques, many challenges remain with, among others, the use of finer-grained segmentation typologies and the consideration of complex, heterogeneous documents such as historical newspapers. Besides, most approaches consider visual features only, ignoring textual signal. In this context, we introduce a multimodal approach for the semantic segmentation of historical newspapers that combines visual and textual features. Based on a series of experiments on diachronic Swiss and Luxembourgish newspapers, we investigate, among others, the predictive power of visual and textual features and their capacity to generalize across time and sources. Results show consistent improvement of multimodal models in comparison to a strong visual baseline, as well as better robustness to high material variance.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Uncontrolled Keywords:historical newspapers; image segmentation; multimodal learning; deep learning; digital humanitites
Language:English
Date:2021
Deposited On:15 Feb 2022 14:47
Last Modified:13 Mar 2024 15:18
Publisher:OMICS Publishing Group
ISSN:2153-0602
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.46298/jdmdh.6107
Related URLs:https://www.zora.uzh.ch/id/eprint/200811/
Project Information:
  • Funder: SNSF
  • Grant ID: CRSII5_173719
  • Project Title: Media Monitoring of the Past
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  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

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