H. Badioze Zaman et al. (Eds.): IVIC 2011, Part I, LNCS 7066, pp. 176–182, 2011. © Springer-Verlag Berlin Heidelberg 2011 Empirical Performance Evaluation of Raster to Vector Conversion with Different Scanning Resolutions Bilal Abdulrahman T. Al-Douri, Hasan S.M. Al-Khaffaf, and Abdullah Zawawi Talib School of Computer Sciences Universiti Sains Malaysia 11800 USM Penang, Malaysia belal_200033@yahoo.com, {hasanm,azht}@cs.usm.my Abstract. Empirical performance evaluation of raster to vector conversion is a means of judging the quality of line detection algorithms. Many factors may affect line detection. This paper aims to study scanning resolution of raster images and its effects on the performance of line detection. Test images with three different scanning resolutions (200, 300, and 400 DPI) are vectorised using available raster to vector conversion software. The Vector Recovery Index scores calculated with reference to the ground truth images and the detected vectors are then obtained. These values are analysed statistically in order to study the effects of different scanning resolutions. From the results, Vextractor is found to be better (on average) compared to VPstudio and Scan2CAD. For all the three resolutions, Vextractor and VPstudio perform better than Scan2CAD. Different scanning resolutions affect the software differently. The performance of Vextractor and VPstudio increases from low resolution to moderate resolution, and then decreases with high resolution. The performance of Scan2CAD decreases with the increase in the resolutions. Keywords: Empirical Performance Evaluation, Raster to Vector Conversion, Vector Recovery Index, Statistical Analysis, Visual Informatics. 1 Introduction Empirical performance evaluation of raster to vector conversion is an important topic in the area of graphics recognition. It is used as a means to judge the quality of line detection algorithms. There are many factors that affect quality of line detection and they may lead to better or poorer line detection rate. Some of these factors have been studied such as noise level, vectorisation software, and cleaning methods [1] and the other factors have yet to be studied such as scanning resolution of raster images. Chhabra and Ihsin [2] used research prototypes and commercial software. The criterion of the performance was EditCost Index. Liu et al. [3] evaluated the performance of several research prototypes. They used different types of noise (Gaussian noise, high frequency, hard pencil, and geometrical noise). VRI was used to measure the performance of the methods. Only solid circular arcs were included in their tests.