Directional Pattern Matching for Character Recognition Revisited
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Flat image recognition in the process of microdevice assembly
Pattern Recognition Letters
Modular neural networks with Hebbian learning rule
Neurocomputing
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
Off-line cursive script recognition: current advances, comparisons and remaining problems
Artificial Intelligence Review
Hi-index | 0.00 |
This paper presents the latest results of handwritten digit recognition on well-known image databases using state-of-the-art featur e extr action and classic action 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.