Parametric classification in domains of characters, numerals, punctuation, typefaces and image qualities

Authors

Osama Ahmed Khan, University of Texas-Pan American

Date of Award

2004

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Computer Science

First Advisor

Dr. Xiaodong Wu

Second Advisor

Dr. John P. Abraham

Third Advisor

Dr. Richard H. Fowler

Abstract

This thesis contributes to the Optical Font Recognition problem (OFR), by developing a classifier system to differentiate ten typefaces using a single English character ‘e’. First, features which need to be used in the classifier system are carefully selected after a thorough typographical study of global font features and previous related experiments. These features have been modeled by multivariate normal laws in order to use parameter estimation in learning. Then, the classifier system is built up on six independent schemes, each performing typeface classification using a different method. The results have shown a remarkable performance in the field of font recognition. Finally, the classifiers have been implemented on Lowercase characters, Uppercase characters, Digits, Punctuation and also on Degraded Images.

Comments

Copyright 2004 Osama Ahmed Khan. All Rights Reserved.

https://go.openathens.net/redirector/utrgv.edu?url=https://www.proquest.com/dissertations-theses/parametric-classification-domains-characters/docview/305038789/se-2?accountid=7119

Granting Institution

University of Texas-Pan American