An Improved Learning Scheme for the Moving Window Classifier for Handwritten Character Recognition

The moving window classifier (MWC) is a simple and efficient classifier structure which, although shown to be capable of promising performance in a variety of tasks such as face recognition, its common application is a tool in text recognition. Various measures have been proposed to improve the MWC classification speed and to reduce memory space requirement. This paper introduces techniques for improving the MWC classification accuracy without losing any of gains previously achieved. These performance enhancement schemes are readily applicable to a range of related classifiers and hence provide a generalized method for enhancement in a variety of tasks.