A drawing recognition system with rule acquisition ability

We propose a new way of constructing a drawing recognition system. The system has the ability of rule acquisition and therefore can be easily adopted to drawings of different specifications. Rule acquisition is realized through an empirical learning module, which constructs decision trees from teacher examples and translates the decision trees into production rules for actual recognition. The teacher examples are stored along with the corresponding environmental parameters so that future modification/expansion becomes much more easier. Since most of the attribute values are continuous-valued so that the construction of decision tree is more time consuming, we propose can improved algorithm for more efficient selection of cut points. Experimental results show that the proposed algorithm for cut point selection in decision tree generation improves the efficiency by up to 6 times while ensuring the optimal result