On-line handwritten character recognition using parallel neural networks

Our goal is to perform handwritten character recognition using a bank of multilayer feedforward neural networks. This paper presents both the front-end and the back-end of such a recognition system. The front-end relies on a data pre-classification scheme based on the concept of segment. A segment can be viewed as a representative building block of handwriting. The back-end hinges on a connectionist approach. Instead of a single large network, a bank of parallel networks is developed to overcome commonly encountered difficulties such as slow training process and requirement for a large amount of training data. The recognition system has been evaluated, on tasks involving (i) discrimination between similarly shaped characters and (ii) recognition of discretely written upper-case characters.