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Machine recognition of independent and contextually constrained contour-traced handprinted characters

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Title: Machine recognition of independent and contextually constrained contour-traced handprinted characters
Author: Toussaint, Godfried T.
Degree: Master of Applied Science - MASc
Program: Electrical and Computer Engineering
Copyright Date: 1969
Subject Keywords Machine translating.;Writing.
Issue Date: 2011-05-18
Publisher University of British Columbia
Series/Report no. UBC Retrospective Theses Digitization Project [http://www.library.ubc.ca/archives/retro_theses/]
Abstract: A contour-tracing technique originally divised by Clemens and Mason was modified and used with several different classifiers of varying complexity to recognize upper case handprinted alphabetic characters. An analysis and comparison of the various classifiers, with the modifications introduced to handle variable length feature vectors, is presented. On independent characters, one easily realized suboptimum parametric classifier yielded recognition accuracies which compare favourably with other published results. Additional simple tests on commonly confused characters improved results significantly as did use of contextual constraints. In addition, the above classifier uses much less storage capacity than a non-parametric optimum Bayes classifier and performs significantly better than the optimum classifier when training and testing data are limited. The optimum decision on a string of m contextually constrained characters, each having a variable-length feature vector, is derived. A computationally efficient algorithm, based on this equation, was developed and tested with monogram, bigram and trigram contextual constraints of English text. A marked improvement in recognition accuracy was noted over the case when contextual constraints were not used, and a trade-off was observed not only between the order of contextual information used and the number of measurements taken, but also between the order of context and the value of a parameter ds which indicates the complexity of the classification algorithm.
Affiliation: Applied Science, Faculty of
URI: http://hdl.handle.net/2429/34648
Scholarly Level: Graduate

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