Multiscale handwritten character recognition using CNN image filters

This paper presents a multi-scale character recognition system consisting of three single-scale recognition systems. The system uses a filter bank of Gabor-type filters implemented by a cellular neural network (CNN). Based on a test set of 26 test characters acting as template and a set consisting of four subsets of 26 unknown handwritten test characters, a maximum 96% and an average 93% correct recognition is provided. This is a considerable improvement over the performance of existing single-scale recognition systems.