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We present a multi resolution scheme for symbol representation and recognition based on statistical shape features. We define a symbol as a set of shape points, each of which is then described by a pyramid of shape context features. The pyramid is constructed by successively partitioning the image surrounding one shape point into increasingly finer sub-regions and computing the local shape context descriptor inside each sub-region. To recognize a symbol, we compute the optimal matching between symbol prototypes and the image region, based on the weighted distance measurements across various scales. We also define an adaptive surround suppression measure that assigns different weights to the shape point depending on the complexity of its surrounding context, so as to reduce the effect of local intersections to shape matching. The experimental results show the effectiveness of the proposed shape context pyramid matching method as well as its promising aspects in handling intersecting symbols.