Authors:
Bruno Côme
1
;
2
;
Maxime Devanne
1
;
Jonathan Weber
1
and
Germain Forestier
1
Affiliations:
1
IRIMAS, University of Haute-Alsace, France
;
2
Duke, Saint-Paul, La Reunion, France
Keyword(s):
Chart Classification, Convolutional Neural Networks, Vision-Language Models, Data Visualization.
Abstract:
Chart image classification is a critical task in automating data extraction and interpretation from visualizations, which are widely used in domains such as business, research, and education. In this paper, we evaluate the performance of Convolutional Neural Networks (CNNs) and Vision-Language Models (VLMs) for this task, given their increasing use in various image classification and comprehension tasks. We constructed a diverse dataset of 25 chart types, each containing 1,000 images, and trained multiple CNN architectures while also assessing the zero-shot generalization capabilities of pre-trained VLMs. Our results demonstrate that CNNs, when trained specifically for chart classification, outperform VLMs, which nonetheless show promising potential without the need for task-specific training. These findings underscore the importance of CNNs in chart classification while highlighting the unexplored potential of VLMs with further fine-tuning, making this task crucial for advancing aut
omated data visualization analysis.
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