Space reconstruction based Biomimetic Pattern Recognition

This paper presents a new scheme for off-line recognition of totally unconstrained handwritten numerals. The work is motivated by two approaches: Affinity Propagation (AP) clustering and Biomimetic Pattern Recognition (BPR). It can relatively classify the samples by calculating the distance to the relative subspace. The training sample space is constructed by the AP algorithm and BPR theory. The posterior probabilities based on the class condition are estimated to reduce the rejection caused by the space overlapping with low misclassification. Experiments have been performed with Concordia University CENPARMI´s handwritten digit database and Nanjing University of Science and Technology´s handwritten amount database. Experimental results indicate that the proposed classifier has a higher recognition rate than the traditional classifiers.