DocumentCode
3029303
Title
Classification of Handwritten Characters by their Symmetry Features
Author
Holland, Sam ; Neville, Richard
Author_Institution
Machine Learning & Optimisation Res. Group, Univ. of Manchester, Manchester, UK
fYear
2009
fDate
28-29 Dec. 2009
Firstpage
316
Lastpage
318
Abstract
We propose a technique to classify characters by two different forms of their symmetry features. The generalized symmetry transform is applied to digits from the USPS data set. These features are then used to train probabilistic neural networks and their performances are compared to the traditional method.
Keywords
handwritten character recognition; learning (artificial intelligence); neural nets; pattern classification; probability; USPS data set; generalized symmetry transform; handwritten character classification; probabilistic neural networks; Artificial neural networks; Character recognition; Neural networks; Neurons; Optical character recognition software; Optical computing; Optical network units; Probability distribution; Smoothing methods; Testing; character; handwritten; networks; neural; probabilistic; recognition; symmetry;
fLanguage
English
Publisher
ieee
Conference_Titel
Advances in Computing, Control, & Telecommunication Technologies, 2009. ACT '09. International Conference on
Conference_Location
Trivandrum, Kerala
Print_ISBN
978-1-4244-5321-4
Electronic_ISBN
978-0-7695-3915-7
Type
conf
DOI
10.1109/ACT.2009.85
Filename
5376666
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