A Survey on Figure Classification Techniques in Scientific Documents
Abstract
Figures visually represent an essential piece of information and provide an effective means to communicate scientific facts. Recently there have been many efforts toward extracting data directly from figures, specifically from tables, diagrams, and plots, using different Artificial Intelligence and Machine Learning techniques. This is because removing information from figures could lead to deeper insights into the concepts highlighted in the scientific documents. In this survey paper, we systematically categorize figures into five classes - tables, photos, diagrams, maps, and plots, and subsequently present a critical review of the existing methodologies and data sets that address the problem of figure classification. Finally, we identify the current research gaps and provide possible directions for further research on figure classification.
Cite
@article{arxiv.2307.05694,
title = {A Survey on Figure Classification Techniques in Scientific Documents},
author = {Anurag Dhote and Mohammed Javed and David S Doermann},
journal= {arXiv preprint arXiv:2307.05694},
year = {2023}
}
Comments
Some contents of this paper appears in the accepted paper - "A Survey and Approach to Chart Classification" at 15th IAPR GREC 2023 at 17th ICDAR 2023, August 21-26, San Jose, USA. arXiv admin note: text overlap with arXiv:2307.04147