Beyond images: data visualization through headline analysis in historical newspaper with computer vision

Michael Kin-Fu Yip; Vincent Wai-Yip Lum

doi:10.1117/12.3017929

8 December 2023 Beyond images: data visualization through headline analysis in historical newspaper with computer vision

Michael Kin-Fu Yip, Vincent Wai-Yip Lum

Author Affiliations +

Proceedings Volume 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023); 1294316 (2023) https://doi.org/10.1117/12.3017929
Event: International Workshop on Signal Processing and Machine Learning (WSPML 2023), 2023, Hangzhou, ZJ, China

Abstract

This paper introduces an innovative method that combines Computer Vision and Deep Learning to extract headlines from a historical newspaper. Through the illustrations from historical newspapers, one of our goals is to use these extracted headlines to support digital humanities. The research goes beyond traditional image analysis by exploring how new digital technologies can facilitate the understanding of newspaper content by visualizing through time and place. The experimental results reveal that our recommended approaches, which involve Optical Character Recognition (OCR) with scraping and Deep Learning Object Detection models, can successfully obtain the required information for more advanced analytics. Due to the distinctive historical and humanities values, we chose "The Hongkong News" from the Hong Kong Early Tabloid Newspaper collection to illustrate the efficacy of our methodology. In addition, we constructed several visualization applications to demonstrate the viability of our suggested approaches.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Michael Kin-Fu Yip and Vincent Wai-Yip Lum "Beyond images: data visualization through headline analysis in historical newspaper with computer vision", Proc. SPIE 12943, International Workshop on Signal Processing and Machine Learning (WSPML 2023), 1294316 (8 December 2023); https://doi.org/10.1117/12.3017929

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