Learning and Upgrading Rules for an Optical Character Recognition System Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InCollection{andre:1997:HEC,

• author = "David Andre",
• title = "Learning and Upgrading Rules for an Optical Character Recognition System Using Genetic Programming",
• booktitle = "Handbook of Evolutionary Computation",
• publisher = "Oxford University Press",
• publisher_2 = "Institute of Physics Publishing",
• year = "1997",
• editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz",
• chapter = "section G8.1",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-7503-0392-1",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
• broken = "doi:10.1201/9781420050387.ptg",
• URL = "https://www.amazon.com/Handbook-Evolutionary-Computation-Computational-Intelligence/dp/0750303921",
• URL = "https://www.worldcat.org/title/handbook-of-evolutionary-computation/oclc/1108947278",
• size = "8 pages",
• abstract = "Rule-based systems used for optical character recognition (OCR) are notoriously difficult to write, maintain, and upgrade. This case study describes a method for using genetic programming (GP) to automatically generate and upgrade rules for an OCR system. Sets of rules for recognizing a single character are encoded as LISP programs and are evolved using GP. The rule sets are programs that evolve to examine a set of preprocessed features using complex constructs including iteration, pointers, and memory. The system was successful at learning rules for large character sets consisting of multiple fonts and sizes, with good generalization to test sets. In addition, the method was found to be successful at updating human-coded rules written in C for new fonts. This research demonstrates the successful application of GP to a difficult, noisy, real-world problem, and introduces GP as a method for learning sets of rules.",
• notes = "invited chapter",

}

Genetic Programming entries for David Andre

Citations