An object oriented computational model for handwritten document recognition

This paper describes a new computational model for a handwritten document recognition system. It consists of a perceptive subsystem that recognizes each character image extracted from a document using a template matching method and a cognitive subsystem that recognizes a series of input character images as a sentence using semantical and syntactical knowledge. Semantical and syntactical knowledge is represented in a concept graph. Receiving character recognition results, the cognitive subsystem specializes general knowledge so that the concept graph represents a recognition result for the document. An object oriented model is used to specialize general knowledge by means of creating an instance from a class. In cases where some characters are recognized incorrectly, abduction is carried out by means of inference from other character recognition results. The document recognition system will be realized by a parallel object oriented model and suitable for massive parallel processing.