Extraction of strokes in handwritten characters

Among the many handwritten character recognition algorithms that have been proposed in the past few years, few of them use models which are able to simulate handwriting. This can be explained by the fact that simulation models require the estimation of strokes starting from statistic images of letters, while crossing and overlapping strokes make this estimation di$cult. The approach we suggest is to e$ciently deal with crossing areas and overlaps using parametric representations of lines and thickness of stroke: a probabilistic model of strokes is described to extract non-overlapping strokes of the image. A bayesian approach using a statistical study and a model of stroke crossing is described that optimizes the reconstruction of crossings and permits to characterize image of letters by robust graphs of curves. ( 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.