Recognition and quality assessment of data charts in mixed-mode documents

Data charts can be used to effectively compress large amounts of complex information and can convey information in an efficient and succinct manner. It is now easier to create data charts by using a variety of automated software systems. These data charts are routinely inserted in text documents and are widely disseminated over many different media. This study addresses the problem of finding goodness of data charts in mixed-mode documents. The quality of the graphics can be used to assist the document development process as well as to serve as an additional criterion for search engines like Google and Yahoo. The quality measures are motivated by principles of visual learning and are based on research in educational psychology and cognitive theories and use attributes of both the graphic and its textual context. We have implemented the approach and evaluated its effectiveness using a set of documents compiled from the Web. Results of a human study shows that the proposed quality measures have a high correlation with the quality ratings of the users for each of the five classes of data charts studied in this research.