With the increasing demand to scan text documents and old books, having a scanner that could automatically detect the orientations of the scanned pages would be greatly beneficial. This paper proposes a fast method to detect orientations based on a support vector machine (SVM), using features developed for each connected component on the scanned page. Results show that the algorithm can achieve an accuracy of 99.2% in orientation detection and 98.2% in script detection for pages scanned at 200 dpi.
As a biologically inspired guess, we consider two stereo information channels. One is the traditional channel that works on the basis of the horizontal disparity between the left and right projections of single points in the 3D scene; this channel carries information regarding the absolute depth of the point. The second channel works on the basis of the projections of pairs of points in the 3D scene and carries information regarding the relative depth of the points; equivalently, for a given azimuth disparity of the points, the channel carries information of the ratio of the orientations of the left and right projections of the line segment between the pair of points.