  Spreadsheets are among the most commonly used file formats for data
management, distribution, and analysis. Their widespread employment makes it
easy to gather large collections of data, but their flexible canvas-based
structure makes automated analysis difficult without heavy preparation. One of
the common problems that practitioners face is the presence of multiple,
independent regions in a single spreadsheet, possibly separated by repeated
empty cells. We define such files as "multiregion" files. In collections of
various spreadsheets, we can observe that some share the same layout. We
present the Mondrian approach to automatically identify layout templates across
multiple files and systematically extract the corresponding regions. Our
approach is composed of three phases: first, each file is rendered as an image
and inspected for elements that could form regions; then, using a clustering
algorithm, the identified elements are grouped to form regions; finally, every
file layout is represented as a graph and compared with others to find layout
templates. We compare our method to state-of-the-art table recognition
algorithms on two corpora of real-world enterprise spreadsheets. Our approach
shows the best performances in detecting reliable region boundaries within each
file and can correctly identify recurring layouts across files.
