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Shape indexing and recognition have received great attention in multimedia processing communities due to the wide range of utilities. In achieving shape representation, most influential methods treat shapes as intrinsic curves, which is not in agreement with the way human vision systems achieve the same task. In this paper, a new framework is developed where a shape is treated as a 2-D region. We first perform an eigen analysis to align P in the standard orientation. Three numbers are generated to indicate the global geometrical nature. Next, according to the two eigen vectors, we partition P into four halves and eight quadrants. Five numbers are then produced for each region to signify its geometrical properties and relation with P. The aggregate of these numbers, 63 in total, is the actual index for P. Recognition is effected by weighted LI distances between shapes. This indexing scheme captures the global geometry of shapes and is resilient to rotations and scales, which are of crucial importance in the perceptive process of human vision systems. It can tolerate occlusions present in most standard shape datasets but is not robust against severe occlusions. Empirical studies conducted on standard synthetic and real-world datasets demonstrate encouraging performances.