Character recognition by neural networks with single-layer training and rejection mechanism
Joonho Lim, Eel-Wan Lee, Soo‐Ik Chae · 2002
For many real applications of pattern classification problems, it is more important to reduce the misclassification rate than to increase the rate of successful classification. In this paper, we propose a single-layer neural network with two rejection mechanisms for character recognition problems, which guarantees a very low misclassification rate. The proposed architecture is a cascaded connection of an SLP network and a simple combinational circuit. Comparing to the MLP network, it yields fast learning and requires a simple hardware architecture. We also introduce a new linearly separable coding scheme for training the SLP network to reduce the misclassification rate. We prepared two databases: one with 135,000 digit patterns and the other with 117,000 letter patterns. Then we applied the proposed method to the classification problem for the databases and results show that the misclassification rate is significantly low with maintaining a high recognition rate.>