A recurrent neural network for partitioning of hand drawn characters into strokes of different orientations

A neural network is described which is intended to extract orientation features that should be used for recognition of hand drawn characters. The network partitions the input hand drawn characters into separate line segments (strokes) according to their orientations. The network consists of several neural layers; each layer serves for extracting strokes of a certain orientation. Every neural layer has one-to-one correspondence with an input screen. The network uses an iterative update procedure which includes interactions of neurons inside each layer through oriented excitatory connections and inhibitory interrelations between the corresponding neurons of different layers. Computer simulation of the network was performed. Experiments showed that the network efficiently classifies all pixels of any hand drawn characters according to the orientations of the strokes constituting these characters and performs, as a result of that, a reasonable segmentation of characters.