Combining Multiple Classifiers For Pen-Based Handwritten Digit Recognition (1996)
| Citations: | 8 - 1 self |
BibTeX
@MISC{Alimoglu96combiningmultiple,
author = {Fevzi Alimoglu and Yrd Doc and Dr. Ethem Alpaydin and Doc Dr and Yagmur Denizhan},
title = {Combining Multiple Classifiers For Pen-Based Handwritten Digit Recognition},
year = {1996}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
Handwriting recognition has attracted enormous scientific interest because of its potential for improved man/machine interfaces. We have designed an on-line handwritten digit recognition system after the examination of different techniques based on statistical and neural pattern recognition approaches. We collected a digit database from 44 people. We use two representations. The dynamic representation is based on constant length feature vectors of equally distanced points on the pen trajectory. The static representation converts the dynamic information to an image similar to images used in off-line recognition tasks.Then, we tested the well known statistical classification method k-nearest neighbor (k-NN) and neural multi-layer perceptron (MLP) and recurrent networks using both representations. Classifiers trained with dynamic and static representations make misclassifications for different samples. We combine them first by forming a feature vector composed of dynamic and static repr...
