Understanding Line Plots Using Bayesian Network

Information graphics, such as bar charts, graphs, plots etc. in scientific documents primarily facilitate better understanding of information. Graphics are a key component in technical documents as they are simplified representations of complex ideas. When the traditional optical character recognition (OCR) systems is used on digitized documents, we lose the ideas conveyed in these information graphics since OCRs typically work only on text. And although in more recent times, tools have been developed to extract information graphics from pdf files, they still do not intelligently interpret the contents of the extracted graphics. We therefore propose a method for identifying the intended messages of line plots using a Bayesian network. We accomplish this by first extracting a dense set of points in from a line plot and then represent the entire line plot as a sequence of trends. We then implement a Bayesian network for reasoning about the messages conveyed by the line plots and their trends. We validate our approach by performing experiments on a dataset obtained from computer science conference publications and evaluate the performance of the network against the messages generated by human end users. The resulting intended message gives holistic information about the line plot(s) as well as lower level information about the trends that make up the plot.