We present a variational framework that integrates the statistical boundary shape models into a Level Set system that is capable of both segmenting and recognizing objects. Since we aim to recognize objects, we trace the active contour and stop it near real object boundaries while inspecting the shape of the contour instead of enforcing the contour to get a priori shape. We get the location of character boundaries and character labels at the system output. We developed a promising local front stopping scheme based on both image and shape information. A new object boundary shape signature model, based on directional Gauss gradient filter responses, was also proposed. The character recognition system employs the new boundary shape descriptor outperformed other well-known boundary signatures such as centroid distance, curvature etc.