Parsing tables by probabilistic modeling of perceptual cues

In this paper, we propose a method for automatically parsing images of tables, focusing in particular on `simple' matrix-like tables with rectilinear layout. Such tables account for over 50% of tables in business documents. The main novelty of the proposed method is that it combines intrinsic properties of table cells with properties of cell separators, as well as table rows, columns, and layout, in a single global objective function. This is in contrast to previous methods which focused on either separators alone or intrinsic cell properties alone. Our method uses a variety of perceptual cues, such as alignment and saliency, to characterize these properties. Candidate parses are evaluated by comparing their likelihoods, and the parse that optimizes the likelihood is selected. The proposed approach deals successfully with a wide variety of tables, as illustrated on a dataset of over 1,000 images.