A new approach for binary line image vectorization

Lines and junctions are principal features for a line image. The spatial relationships established among them are usually employed in applications such as OCR and architectural blueprint vectorization. The conventional thinning techniques often suffered the pitfall of spurious junction points that are crucial features to derive. The liability of the shape of the skeleton resulting from multiple fork points connected through several short branches will impede the further recognition stage. In this paper, a new approach which differentiates from the conventional thinning algorithms in vectorizing a raster line image is presented. This vectorization algorithm takes the ensemble of pixels within the line segments collectively as legitimate candidates in deciding the vectorized representation. This method can not only segment the lines and junctions but also construct their spatial relationships. A maximal inscribing circle (MIC) concept is introduced to derive the directions of line segments. An iterative procedure is developed to identify each line segment and the corresponding junctions. Experimental studies comparing the performance of a conventional thinning method with that of our MIC algorithm are performed using the flow diagram as test images. The results demonstrate that our approach is computation efficient, robust and may render optimal multipixel-width vectorized line representation at user's discretion.