Resolving multifont character confusion with neural networks

A set of feedforward neural networks and a rule invocation procedure are added to a conventional character recognition system to help resolve similar character confusion and this approach has the advantage of automating the feature selection and feature extraction processes, and it is more robust to noise.

Abstract

The existence of similar characters contributes to most of the errors made by multifont character recognition systems. A set of feedforward neural networks and a rule invocation procedure are added to a conventional character recognition system to help resolve similar character confusion. Compared to previous methods, the approach has the advantage of automating the feature selection and feature extraction processes, and it is more robust to noise as confirmed by experiments. The contributions are a snowball training algorithm and a smoothing technique, both are modifications to the back-propagation training algorithm. The snowball training algorithm presents training data in a better sequence to remedy the training convergence problem. The smoothing technique seeks a solution with smooth connection weights during training to improve a network's generalization capability. Combining the two proposed techniques increases the average recognition rate of the rule nets to 99.65 from 87.35%.