Detection of Curved and Straight Segments from Gray Scale Topography

Abstract

Conventional structural pattern recognition methods that rely on thinning approximate the skeleton by polygons and proceed from that step to recognition. Polygonal approximations have the disadvantage that they introduce significant ambiguities. In particular, vertices may correspond either to real corners or to approximations of smooth arcs. We propose a method that relies on topographic features and, in particular, ridge lines. The information about ridge line directions obtained from the underlying surface of the gray tone is used to discriminate between arcs and straight lines. Normally, ridge lines are centered within character strokes, forming skeleton-like ribbons with generally no more than three pixels in width. For each ridge pixel, the tangent direction of the ridge line at the pixel is calculated. These computed tangent directions are then used in the detection of sharp corners and junctions, in the line tracking process, and in the feature decomposition process. Decomposition is achieved using curvature primitives and singular points. The result of the method is a relational feature graph which gives a compact and flexible description of the shapes of the objects in the input image. By not using a conventional thinning algorithm and performing arc and straight line decomposition without a usual polygonal approximation step, our method is able to reduce some artifacts of conventional thinning and to eliminate completely the ambiguities resulting from polygonal approximations.