BESAC: Binary External Symmetry Axis Constellation for unconstrained handwritten character recognition

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 Odia and IITBBS Odia numeral, and a newly created IITBBS Odia character database). The average time for classifying an unknown handwritten character is reported to be significantly less than the existing methods. The average recognition accuracy using the proposed approach is proved to outperform the state-of-the-art accuracy results on ISI Kolkata Odia numeral database (99.35%), IITBBS Odia numeral (98.9%), ISI Kolkata Bangla numeral database (99.48%) and IITBBS Odia character (95.01%) database.