Improvements to Uncalibrated Feature-Based Stereo Matching for Document Images by Using Text-Line Segmentation

Document images prove to be a difficult case for standard stereo correspondence approaches. One of the major problem is that document images are highly self-similar. Most algorithms try to tackle this problem by incorporating a global optimization scheme, which tends to be computationally expensive. In this paper, we show that incorporation of layout information into the matching paradigm, as a grouping entity for features, leads to better results in terms of robustness, efficiency, and ultimately in a better 3D model of the captured document, that can be used in various document restoration systems. This can be seen as a divide and conquer approach that partitions the search space into portions given by each grouping entity and then solves each of them independently. As a grouping entity text-lines are preferred over individual character blobs because it is easier to establish correspondences. Text-line extraction works reasonably well on stereo image pairs in the presence of perspective distortions. The proposed approach is highly efficient and matches obtained are more reliable. The claims are backed up by showing their practical applicability through experimental evaluations.