19th Century United States Newspaper Advert images with 'illustrated' or 'non illustrated' labels
Contributors
Data collector:
Description
The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (chroniclingamerica.loc.gov/).
[The Newspaper Navigator dataset] consists of extracted visual content for 16,358,041 historic newspaper pages in Chronicling America. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the Beyond Words crowdsourcing project.
One of these categories is 'advertisements. This dataset contains a sample of these images with additional labels indicating if the advert is 'illustrated' or 'not illustrated'.
The data is organised as follows:
- The images themselves can be found in `images.zip`
- `newspaper-navigator-sample-metadata.csv` contains metadata about each image drawn from the Newspaper Navigator Dataset.
- `ads.csv` contains the labels for the images as a CSV file
- `sample.csv` contains additional metadata about the images (based on the newspapers those images came from).
This dataset was created for use in an under-review Programming Historian tutorial (http://programminghistorian.github.io/ph-submissions/lessons/computer-vision-deep-learning-pt1) The primary aim of the data was to provide a realistic example dataset for teaching computer vision for working with digitised heritage material. The data is shared here since it may be useful for others. This data documentation is a work in progress and will be updated when the Programming Historian tutorial is released publicly.
The metadata CSV file contains the following columns:
- filepath
- pub_date
- page_seq_num
- edition_seq_num
- batch
- lccn
- box
- score
- ocr
- place_of_publication
- geographic_coverage
- name
- publisher
- url
- page_url
- month
- year
- iiif_url
Files
ads.csv
Additional details
Related works
- Is derived from
- Journal article: https://arxiv.org/abs/2005.01583 (URL)
- Requires
- Software: 10.5281/zenodo.5537185 (DOI)
Funding
- UK Research and Innovation
- Living with Machines AH/S01179X/1