Handwritten digit recognition using state-of-the-art techniques

This paper presents the latest results of handwritten digit recognition on well-known image databases using state-of-the-art feature extraction and classification techniques. The tested databases are CENPARMI, CEDAR, and MNIST. On the test dataset of each database, 56 recognition accuracies are given by combining 7 classifiers with 8 feature vectors. All the classifiers and feature vectors give high accuracies. Among the features, the chaincode feature and gradient feature show advantages, and the profile structure feature shows efficiency as a complementary feature. In comparison of classifiers, the support vector classifier with RBF kernel (SVC-rbf) gives the highest accuracy but is extremely expensive in storage and computation. Among the non-SV classifiers, the polynomial classifier (PC) performs best, followed by a learning quadratic discriminant function (LQDF) classifier. The results are competitive compared to previous ones and they provide a baseline for evaluation of future works.