This paper presents a method for vectorizing the graphical parts of paper-based line drawings. The method consists of separating the input binary image into layers of homogeneous thickness, skeletonizing each layer, segmenting the skeleton by a method based on random sampling, and simplifying the result. The segmentation method is robust with a best bound of 50 percent noise reached for indefinitely long primitives. Accurate estimation of the recognized vector's parameters is enabled by explicitly computing their feasibility domains. Theoretical performance analysis and expression of the complexity of the segmentation method are derived. Experimental results and comparisons with other vectorization systems are also provided.

This paper deals with performance evaluation of raster-to-vector conversion algorithms. We briefly review the past work, and focus our attention on a method proposed by Phillips and Chhabra (1999). We show that the matching algorithm used in their method can be improved by introducing new metrics and reformulating the problem as a combinatorial optimization scheme. Improvements brought by our approach are the reduction of the number of utilized thresholds to one, and a gain of stability in the obtained results. We also propose an algorithm to solve the scheme, and compare the evaluation results obtained with both methods on real CAD drawings.