In this paper we propose an algorithm for contour-based object detection in cluttered images. Contour of an object shape is approximated as a set of line segments and object detection is framed as matching contour segments of an image (i.e., an edge image) to a boundary model of an object (i.e., a line drawing). Local shape is abstracted as a group of k-adjacent segments. We use a multi-level shape description (with different k's) to capture complexity variations in local shape. Between images, shape descriptors are matched to give inter-shape correspondences and within images the underlying segment grouping enforces intra-shape contextual constraints. We use an efficient relaxation labeling approach that integrates these shape cues to qualify a contour match. To this end, we propose a novel framework that solves the problem of object detection as a contour segments correspondence problem. We then demonstrate the efficacy of the method for detecting various objects in cluttered images by comparing them to simple line drawings.