Background grid extraction from historical hand-drawn cadastral maps

We tackle a novel problem of detecting background grids in hand-drawn cadastral maps. Grid extraction is necessary for accessing and contextualizing the actual map content. The problem is challenging since the background grid is the bottommost map layer that is severely occluded by subsequent map layers. We present a novel automatic method for robust, bottom-up extraction of background grid structures in historical cadastral maps. The proposed algorithm extracts grid structures under significant occlusion, missing information, and noise by iteratively providing an increasingly refined estimate of the grid structure. The key idea is to exploit periodicity of background grid lines to corroborate the existence of each other. We also present an automatic scheme for determining the ‘gridness’ of any detected grid so that the proposed method self-evaluates its result as being good or poor without using ground truth. We present empirical evidence to show that the proposed gridness measure is a good indicator of quality. On a dataset of 268 historical cadastral maps with resolution \(1424\times 2136\) pixels, the proposed method detects grids in 247 images yielding an average root-mean-square error (RMSE) of 5.0 pixels and average intersection over union (IoU) of 0.990. On grids self-evaluated as being good, we report average RMSE of 4.39 pixels and average IoU of 0.991. To compare with the proposed bottom-up approach, we also develop three increasingly sophisticated top-down algorithms based on RANSAC-based model fitting. Experimental results show that our bottom-up algorithm yields better results than the top-down algorithms. We also demonstrate that using detected background grids for stitching different maps is visually better than both manual and SURF-based stitching.