Handwritten Digit Recognition Using Multiple Feature Extraction Techniques and Classifier Ensemble
Rafael M. O. Cruz, George D. C. Cavalcanti, Tsang Ing Ren · Espace ÉTS (ETS) · 2010
It is herein proposed a handwritten digit recognition system which uses multiple feature extraction methods and classifier ensemble. The combination of the feature extraction methods is motivated by the observation that different feature extraction algorithms have a better discriminative power for some types of digits. Six features sets were extracted, two proposed by the authors and four published in previous works. It is shown that combining these feature sets is sufficient to achieve high recognition rates. Several combination schemes were tested, showing good results. A scheme using neural networks as a combiner achieved a recognition rate of 99.68%, the highest one on the MNIST database.