Using A Blackboard Architecture For Control In A Knowledge-Based Document Understanding System

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Abstract
This paper describes a knowledge-based document understanding system that makes inferences about document images. This document understanding system basically consists of a rule-based reasoning system, a set of image processing modules, and a blackboard control system. The rule-based system controls the classification of the blocks in the document based on the various characteristics of the different blocks that are extracted by the low-level image processing routines. The interaction between the rule-based system and the image processing routines is controlled by the blackboard control architecture. This blackbord control system contains domain and control blackboards that store image data as well as information about intermediate processing states, and a blackboard control mechanism that monitors the invocation of various image processing operations on the document image and keeps track of the current processing status of the rule-based system. The exchange of control between the rule-based system and the blackboard control architecture brings up some interesting issues about opportunistic reasoning versus explicit control in reasoning, which are discussed in this paper.

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