Junction-based table detection in camera-captured document images

In this paper, we present a method that locates tables and their cells in camera-captured document images. In order to deal with this problem in the presence of geometric and photometric distortions, we develop new junction detection and labeling methods, where junction detection means to find candidates for the corners of cells, and junction labeling is to infer their connectivity. We consider junctions as the intersections of curves, and so we first develop a multiple curve detection algorithm. After the junction detection, we encode the connectivity information (including false detection) between the junctions into 12 labels, and design a cost function reflecting pairwise relationships as well as local observations. The cost function is minimized via the belief propagation algorithm, and we can locate tables and their cells from the inferred labels. Also, in order to handle multiple tables in a single page, we propose a table area detection method. Our method is based on the well-known recursive X-Y cut, however, we modify the method so that we can also deal with curved seams caused by the geometric distortions. For the evaluation of our method, we build a data set that includes a variety of camera-captured table images and make the set publicly available. Experimental results on the set show that our method successfully locates tables and their cells in camera-captured images.