Handwriting style classification

DEHKORDI, M., SHERKAT, N. and ALLEN, T., 2003. Handwriting style classification. International Journal on Document Analysis and Recognition, 6 (1), pp. 55-74. ISSN 1433-2833

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Abstract

This paper describes an independent handwriting style classifier that has been designed to select the best recognizer for a given style of writing. For this purpose a definition of handwriting legibility has been defined and a method implemented that can predict this legibility. The technique consists of two phases. In the feature-extraction phase, a set of 36 features is extracted from the image contour. In the classification phase, two nonparametric classification techniques are applied to the extracted features in order to compare their effectiveness in classifying words into legible, illegible, and middle classes. In the first method, a multiple discriminant analysis (MDA) is used to transform the space of extracted features (36 dimensions) into an optimal discriminant space for a nearest mean based classifier. In the second method, a probabilistic neural network (PNN) based on the Bayes strategy and nonparametric estimation of probability density function is used. The experimental results show that the PNN method gives superior classification results when compared with the MDA method. For the legible, illegible, and middle handwriting the method provides 86.5% (legible/illegible), 65.5% (legible/middle), and 90.5% (middle/illegible) correct classification for two classes. For the three-class legibility classification the rate of correct classification is 67.33% using a PNN classifier.

Item Type: Journal article
Alternative Title: Style classification of handwriting
Description: The original publication is available at www.springerlink.com
Publication Title: International Journal on Document Analysis and Recognition
Creators: Dehkordi, M., Sherkat, N. and Allen, T.
Publisher: Springer Verlag
Place of Publication: Berlin/Heidelberg
Date: 2003
Volume: 6
Number: 1
ISSN: 1433-2833
Identifiers:
NumberType
10.1007/s10032-003-0101-4DOI
Rights: © Springer-Verlag 2003
Divisions: Schools > School of Science and Technology
Depositing User: EPrints Services
Date Added: 09 Oct 2015 10:41
Last Modified: 23 Aug 2016 09:11
URI: http://irep.ntu.ac.uk/id/eprint/16571

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