Pattern skeletonization using run-length-wise processing for intersection distortion problem
Existing skeletonization methods are largely pixel-wise methods which present problems at line pattern intersection regions. Challenged by the problems, we have developed a run-length-wise method in vectorizing line pattern intersections and solving intersection distortion problem in skeletonization. We experimented the method with handwritten Chinese character and numeral character skeletonization to find points of convergence and divergence and to link them into meaningful sets of data. Using these sets of data we construct windows around intersection regions and replace distorted intersection with straight line intersection. Thus we overcome the distortion problem and at the same time retain the smooth property of pixel-wise thinning.
binary image character recognition geometric performance intersection detection line pattern thinning/skeletonization run-length processing