Object Detection in Heritage Archives using a Human-in-Loop Concept

KASTURI, Surya, SHENFIELD, Alex, ROAST, Chris, LE PAGE, Danny and BROOME, Alice (2024). Object Detection in Heritage Archives using a Human-in-Loop Concept. In: Proceedings of the 22nd UK Workshop on Computational Intelligence. Advances in Intelligent Systems and Computing . Springer.

[img] PDF (RRS OA policy applies, make available on publication)
2023_ukci_object_detection_in_archival_documents_with_hil.pdf - Accepted Version
Restricted to Repository staff only
Creative Commons Attribution.

Download (12MB)
Official URL: https://link.springer.com/book/9783031475078
Related URLs:

Abstract

The use of object detection has become common within the area of computer vision and has been considered essential for a numerous applications. Currently, the field of object detection has undergone significant development and can be broadly classified into two categories: traditional machine learning methods that employ diverse computer vision techniques, and deep learning methods. This paper proposes a methodology that incorporates the human-in-loop feedback concept to enhance the deep learning object detection capabilities of pre-trained models. These Deep Learning models were developed using a custom humanities and social science dataset that was obtained from the British Online Archives collections database.

Item Type: Book Section
Additional Information: Due to be published 22 Jan 2024 Series ISSN: 2194-5365 UKCI the 22nd UK Workshop on Computational Intelligence, Aston University, 6-8 September 2023.
SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 12 Sep 2023 16:05
Last Modified: 26 Sep 2023 12:59
URI: https://shura.shu.ac.uk/id/eprint/32356

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics