Training Tangent Similarities with N-SVM for Alphanumeric Character Recognition

This paper proposes a fast and robust system for handwritten alphanumeric character recognition. Specifically, a neural SVM (N-SVM) combination is adopted for the classification stage in order to accelerate the running time of SVM classifiers. In addition, we investigate the use of tangent similarities to deal with data variability. Experimental analysis is conducted on a database obtained by combining the well known USPS database with C-Cube uppercase letters where the N-SVM combination is evaluated in comparison with the One-Against-All implementation. The results indicate that the N-SVM system gives the best performance in terms of training time and error rate.