  Most search engines index the textual content of documents in digital
libraries. However, scholarly articles frequently report important findings in
figures for visual impact and the contents of these figures are not indexed.
These contents are often invaluable to the researcher in various fields, for
the purposes of direct comparison with their own work. Therefore, searching for
figures and extracting figure data are important problems. To the best of our
knowledge, there exists no tool to automatically extract data from figures in
digital documents. If we can extract data from these images automatically and
store them in a database, an end-user can query and combine data from multiple
digital documents simultaneously and efficiently. We propose a framework based
on image analysis and machine learning to extract information from 2-D plot
images and store them in a database. The proposed algorithm identifies a 2-D
plot and extracts the axis labels, legend and the data points from the 2-D
plot. We also segregate overlapping shapes that correspond to different data
points. We demonstrate performance of individual algorithms, using a
combination of generated and real-life images.
