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We propose a novel perception driven feature extraction called binary external symmetry axis constellation (BESAC) and a fast Boolean matching character recognition technique. A constellation model using a set of external symmetry axes which are perceptually significant can uniquely represent a handwritten character pattern. This model generates two histograms of orientations that are binary coded and concatenated to produce the proposed BESAC feature. A two stage classification strategy is adopted where a parallel Hamming Distance dissimilarity matching is performed on the extracted BESAC feature to achieve fast recognition along with perceptual closure part detection on look-alike characters. We adopt a 10-fold cross validation strategy to evaluate the performance of our algorithm on two major Indian languages, Bangla and Odia with four benchmark databases (ISI Kolkata Bangla numeral, ISI Kolkata …