Many factors, such as noise level in the original image and the noise-removal methods that clean the image prior to performing a vectorization, may play an important role in affecting the line detection of raster-to-vector conversion methods. In this paper, we propose an empirical performance evaluation methodology that is coupled with a robust statistical analysis method to study many factors that may affect the quality of line detection. Three factors are studied: noise level, noise-removal method, and the raster-to-vector conversion method. Eleven mechanical engineering drawings, three salt-and-pepper noise levels, six noise-removal methods, and three commercial vectorization methods were used in the experiment. The Vector Recovery Index (VRI) of the detected vectors was the criterion used for the quality of line detection. A repeated measure ANOVA analyzed the VRI scores. The statistical analysis shows that all the studied factors affected the quality of line detection. It also shows that two-way interactions between the studied factors affected line detection.

This paper presents a new line extraction method to capture vectors based on contours and skeletons from line drawing raster images in which the lines are touched by characters or other lines. Conventionally, two line extraction methods have generally been used. One is a thinning method. The other is a medial line extraction method based on parallel pairs of contours. The thinning method tends to distort the extracted lines, especially at intersections and corners. On the other hand, the medial line extraction method has a poor capability as regards capturing correct lines at intersections. Contours are able to maintain edge shapes well, while skeletons preserve topological features; thus, a combination of these features effectively leads to the best fitting line. In the proposed method, the line which best fits the original image is selected from among various candidate lines. The candidates are created from several merged short skeleton fragments located between pairs of short contour fragments. The method is also extended to circular arc fitting. Experimental results show that the proposed line fitting method is effective.