A symbol recognition system

A hierarchical neural network approach is presented for the automatic conversion of image documents (ACID), which specifically describes a prototype symbol recognition system (SRS) for automatic computer processing of electrical engineering drawings. This approach achieves a significant reduction of human involvement in the symbol model encoding and recognition processes in contrast to such traditional approaches based on thinning, line tracing, and other structural feature extraction techniques. A set of image intensity moments, which are invariant to geometric transformations, is used as features. A hierarchical neural classifier demonstrates faster and more accurate capabilities for model encoding and recognition. The test results from hand-drawn images by using templates achieves a recognition rate of 98.5% on training symbols and 89% on test symbols