Title :
Vector quantization for recognition of hand written numerals
Author_Institution :
Hughes Res. Labs., Malibu, CA, USA
Abstract :
An extremely simple, highly parallel vector quantization method far recognizing hand written numerals is described. A perceptron learning rule was used in training model numerals composed of simple, local features. 120 images per character were used to train the system; a different set of 100 images per character was used in testing. In the 32 experiments performed to examine the effects of various feature-related parameters, a maximum correct classification rate of 96.7% was observed
Keywords :
character recognition; image coding; learning (artificial intelligence); neural nets; vector quantisation; VQ; correct classification rate; feature-related parameters; hand written numerals recognition; local features; parallel vector quantization; perceptron learning rule; testing; training model; Computer vision; Convolution; Detectors; Feature extraction; Laboratories; Pixel; Smoothing methods; System testing; Vector quantization; Writing;
Conference_Titel :
Signals, Systems and Computers, 1993. 1993 Conference Record of The Twenty-Seventh Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
0-8186-4120-7
DOI :
10.1109/ACSSC.1993.342335
