A multiscale approach for recognizing complex annotations in engineering documents

Andrew Laine, William E. Ball, Arun Kumar

Abstract: A novel method for character recognition targeted at complex annotations found in engineering documents is presented. A feasibility study is described in which characters extracted from engineering drawings were recognized without error from a class of 36 distinct alphanumeric patterns by a neural network classifier trained with multiscale representations. An incremental strategy is presented for resolution which relies upon the continuity between hierarchical levels of a novel multiscale decomposition. The authors observed a 16-fold reduction in the amount of information needed to represent each character for recognition. These results suggest high reliability at a reduced cost of representation.<