In our previous study, we defined the branch length similarity (BLS) entropy for a simple network consisting of a single node and numerous branches. As the first application of this entropy to characterize shapes, the BLS entropy profiles of 20 battle tank shapes were calculated from simple networks created by connecting pixels in the boundary of the shape. The profiles successfully characterized the tank shapes through a comparison of their BLS entropy profiles. Following the application, this entropy was used to characterize human’s emotional faces, such as happiness and sad, and to measure the degree of complexity for termite tunnel networks. These applications indirectly indicate that the BLS entropy profile can be a useful tool to characterize networks and shapes. However, the ability of the BLS entropy in the characterization depends on the image resolution because the entropy is determined by the number of nodes for the boundary of a shape. Higher resolution means more nodes. If the entropy is to be widely used in the scientific community, the effect of the resolution on the entropy profile should be understood. In the present study, we mathematically investigated the BLS entropy profile of a shape with infinite resolution and numerically investigated the variation in the pattern of the entropy profile caused by changes in the resolution change in the case of finite resolution.