Shape perception varies depending on many factors. For example, presenting a stimulus in the periphery often yields a different appearance compared with its foveal presentation. However, how exactly shape appearance is altered under different conditions remains elusive. One reason for this is that studies typically measure identification performance, leaving details about target appearance unknown. The lack of appearance-based methods and general challenges to quantify appearance complicate the investigation of shape appearance. Here, we introduce Geometrically Restricted Image Descriptors (GRIDs), a method to investigate the appearance of shapes. Stimuli in the GRID paradigm are shapes consisting of distinct line elements placed on a grid by connecting grid nodes. Each line is treated as a discrete target. Observers are asked to capture target appearance by placing lines on a freely viewed response grid. We used GRIDs to investigate the appearance of letters and letter-like shapes. Targets were presented at 10° eccentricity in the right visual field. Gaze-contingent stimulus presentation was used to prevent eye movements to the target. The data were analyzed by quantifying the differences between targets and response in regard to overall accuracy, element discriminability, and several distinct error types. Our results show how shape appearance can be captured by GRIDs, and how a fine-grained analysis of stimulus parts provides quantifications of appearance typically not available in standard measures of performance. We propose that GRIDs are an effective tool to investigate the appearance of shapes.