Title :
A symbol recognition system
Author :
Cheng, T. ; Khan, J. ; Liu, H. ; Yun, D.Y.Y.
Author_Institution :
Dept. of Electr. Eng., Hawaii Univ., Manoa, HI, USA
Abstract :
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
Keywords :
document image processing; hierarchical systems; image classification; image recognition; neural nets; ACID; SRS; automatic computer processing; automatic conversion of image documents; electrical engineering drawings; geometric transformations; hand-drawn images; hierarchical neural classifier; hierarchical neural network; human involvement; image intensity moments; line tracing; model encoding; prototype symbol recognition system; structural feature extraction; symbol recognition system; templates; thinning; Computer networks; Electrical engineering; Encoding; Engineering drawings; Humans; Image converters; Image recognition; Neural networks; Prototypes; Testing;
Conference_Titel :
Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on
Conference_Location :
Tsukuba Science City
Print_ISBN :
0-8186-4960-7
DOI :
10.1109/ICDAR.1993.395587
