Abstract
Statistical moment invariants were used to generate a feature space for classifying images of text characters. The feature vector of a given letter is invariant to changes in scale, position, rotation, and contrast in the image. Test character images were generated by simulated optical blurring. Images were classified by calculating the distance between the feature vector of a given test character and that of each reference character. The test character was identified as the reference character for which the distance between feature vectors is a minimum. Significantly blurred characters were classified correctly using this method.
Library of Congress Subject Headings
Optical character recognition devices; Pattern recognition systems
Publication Date
6-28-1993
Document Type
Thesis
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Advisor
Easton, Roger
Advisor/Committee Member
Johnston, Robert
Advisor/Committee Member
Salvaggio, Carl
Recommended Citation
Hanson, Adam, "Character recognition of optically blurred textual images using moment invariants" (1993). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/2795
Campus
RIT – Main Campus
Comments
Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works. Physical copy available through RIT's The Wallace Library at: TA1640.H35 1993