- Library Home /
- Search Collections /
- Open Collections /
- Browse Collections /
- UBC Theses and Dissertations /
- Experiments in character recognition using linear and...
Open Collections
UBC Theses and Dissertations
UBC Theses and Dissertations
Experiments in character recognition using linear and quadratic filters DeMarco, John Francis
Abstract
This thesis describes the simulation of a character recognition system using rive filter designs based on probabilistic models of character patterns. Four of the designs yield linear filters. Of these, three are based on variations of a Gaussian model. The fourth is based on the assumption of independent binary-valued features. The latter design is shown to produce higher recognition rates than any of the others when tested on Munson's multi-author hand-printed characters. This filter design is also tested on two subsets of the Cornell machine-printed data base. The fifth filter design is a special case of a quadratic filter, based on a Gaussian model in which spatially stationary covariance statistics are assumed. This assumption results in a filter structure consisting of a linear operation on the pattern vector plus a linear operation on the autocorrelation vector of the pattern. This filter design is found to achieve lower performance than the best linear filter design when tested on Munson's characters, and nearly equal performance on the Cornell characters. However, there are indications that a filter of this structure could achieve higher performance for some choice of filter coefficients.
Item Metadata
Title |
Experiments in character recognition using linear and quadratic filters
|
Creator | |
Publisher |
University of British Columbia
|
Date Issued |
1980
|
Description |
This thesis describes the simulation of a character recognition system using rive filter designs based on probabilistic models of character patterns.
Four of the designs yield linear filters. Of these, three are based on variations of a Gaussian model. The fourth is based on the assumption of independent binary-valued features. The latter design is shown to produce higher recognition rates than any of the others when tested on Munson's multi-author hand-printed characters. This filter design is also tested on two subsets of the Cornell machine-printed data base.
The fifth filter design is a special case of a quadratic filter, based on a Gaussian model in which spatially stationary covariance statistics
are assumed. This assumption results in a filter structure consisting of a linear operation on the pattern vector plus a linear operation on the autocorrelation vector of the pattern. This filter design is found to achieve lower performance than the best linear filter design when tested on Munson's characters, and nearly equal performance on the Cornell characters.
However, there are indications that a filter of this structure could achieve higher performance for some choice of filter coefficients.
|
Genre | |
Type | |
Language |
eng
|
Date Available |
2010-03-22
|
Provider |
Vancouver : University of British Columbia Library
|
Rights |
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.
|
DOI |
10.14288/1.0065442
|
URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
|
Campus | |
Scholarly Level |
Graduate
|
Aggregated Source Repository |
DSpace
|
Item Media
Item Citations and Data
Rights
For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use.