Abstract:
"Sunburst charts are a popular data visualization tool used to represent hierarchical data
structures. They allow users to view the data in a circular format where the outer rings represent
higher-level categories, and the inner rings represent lower-level sub-categories.
However, reading sunburst charts can be difficult to read due to their circular layout and nested
hierarchy. The viewer must decipher the size and colour of each wedge and navigate the
different levels of categories. Additionally, too many categories or unevenly distributed data can
result in clutter and confusion.
By extracting summary data from a sunburst chart, it is possible to quickly identify key trends
and patterns in the data, such as which categories are the most significant or which subcategories
are the most popular. This can be especially useful for business analysts and marketers who need
to quickly analyze large amounts of data and make informed decisions based on the insights they
uncover. In addition, generating a summary of a sunburst chart by extracting data can help to save
time and improve efficiency. Rather than having to manually sift through a large amount of data
to identify patterns and trends, analysts can use automated tools to extract the most relevant
information and focus their analysis on the areas that are most important.
According to the research papers, there is no existing tool for extracting data and generating
summaries of sunburst charts. As a result, the author opted to implement a system that utilizes
image processing and optical character recognition (OCR) to extract the data and generate the
summary for these charts. Although there were multiple methods available for developing such a
project, the author chose to use image processing and OCR."
