In many vision applications, there is a great demand for an edge detector which can produce edge maps with very different characteristics in nature, so that one of these edge maps may meet the requirements of the problem under consideration. Unfortunately it is not evident how to choose the desired or the optimum edge maps from these solutions that the edge detector offers. The proposed solutions are usually too general that cannot be easily adapted to the application needs by tuning edge detection parameters. One edge detector that we have studied in this study is Generalized Edge Detector which is capable of producing edges with very different characteristics. Although the edge maps based on this representation are reasonable, no one set of scale parameters alone yields a solution close to the desired edges. In this study, we have developed powerful edge operators and have used them under a goal-based edge detection framework Proposed framework is a two-stage process. First, user marks some pixels in the database as edge and non-edge pixels. Then feature vectors comprised of filter responses to G-Filters at different scales are extracted at these marked pixels. Edge detection problem is imposed as two-class classification problem. Support vector machine (SVM) is used in the experiments. Classifier itself is not adequate to extract desired edges for the application under consideration. In the second stage continuous edges are treated as one contour. Then contours are matched with the contours in the training set. Only matched contours are kept and the other contours are eliminated. The purpose of the first stage is to keep only prominent edges and remove irrelevant edges with respect to the application. The classifier decides which discontinuity is prominent or irrelevant. Experimental studies on real license plate images show that the proposed edge detector can successfully detects edges only on license plate regions.