  Charts represent an essential source of visual information in documents and
facilitate a deep understanding and interpretation of information typically
conveyed numerically. In the scientific literature, there are many charts, each
with its stylistic differences. Recently the document understanding community
has begun to address the problem of automatic chart understanding, which begins
with chart classification. In this paper, we present a survey of the current
state-of-the-art techniques for chart classification and discuss the available
datasets and their supported chart types. We broadly classify these
contributions as traditional approaches based on ML, CNN, and Transformers.
Furthermore, we carry out an extensive comparative performance analysis of
CNN-based and transformer-based approaches on the recently published CHARTINFO
UB-UNITECH PMC dataset for the CHART-Infographics competition at ICPR 2022. The
data set includes 15 different chart categories, including 22,923 training
images and 13,260 test images. We have implemented a vision-based transformer
model that produces state-of-the-art results in chart classification.
