Title :
Learning and upgrading rules for an OCR system using genetic programming
Author_Institution :
Stanford Univ., CA, USA
Abstract :
Rule-based systems used for optical character recognition (OCR) are notoriously difficult to write, maintain, and upgrade. The paper describes a method for using genetic programming (GP) to evolve and upgrade rules for an OCR system. The language of the evolved programs was designed such that human hand-coded rules can be included into the initial population in order to upgrade for a new font. The system was successful at learning rules for large character sets consisting of multiple fonts and sizes, with very good generalization to test sets. In addition, the method was found to be successful at updating hand-coded rules written in C for new fonts. This research demonstrates the successful application of GP to a difficult, noisy, real-world problem
Keywords :
character sets; genetic algorithms; knowledge based systems; learning (artificial intelligence); linear programming; optical character recognition; search problems; OCR system; evolved programs; genetic programming; human hand-coded rules; large character sets; multiple fonts; new fonts; optical character recognition; real-world problem; rule-based systems; Automatic control; Character recognition; Computer vision; Detectors; Electric breakdown; Genetic programming; Humans; Knowledge based systems; Optical character recognition software; System testing;
Conference_Titel :
Evolutionary Computation, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the First IEEE Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1899-4
DOI :
10.1109/ICEC.1994.349906