CLUSTER NETWORK FOR RECOGNITION OF HANDWRITTEN, CURSIVE SCRIPT CHARACTERS

A neural net structure was developed to recognize isolated cursive script characters. Characters were composed of features suggested by an established model of handwriting. The model assumes that handwritten characters are formed from a limited number of primitive hand motions characterized by the X-Y oscillations in the vertical (Y) and horizontal (X) directions. Consistent with this model, representation for handwritten characters contain features that are invariant under rotation, translation, changes in size and/or style. Besides their applicability to categorization of handwritten characters, these features are also assumed to control neural ''motor'' activities during writing. The initial application of a single backward error propagation network to handwritten script recognition resulted in slow learning and was limited to approximately 50% correct classification. Changing the architecture to a cluster of smaller networks and adding some simple heuristics improved performance dramatically. In its current form, the approach yields recognition up to 80% for our test data.