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letter-recognition's Introduction

Letter Recognition

The objective is to identify each of a large number of black-and-white rectangular pixel displays as one of the 26 capital letters in the English alphabet. The character images were based on 20 different fonts and each letter within these 20 fonts was randomly distorted to produce a file of 20.000 unique stimuli. Each stimulus was converted into 16 primitive numerical attributes (statistical moments and edge counts) which were then scaled to fit into a range of integer values from 0 through 15.

Data Set Information

Data Set Characteristics Multivariate
Attribute Characteristics Integer
Number of Attributes 16
Number of Instances 20.000
Associated Tasks Classification

Results

We are going to measure the accuracy rate into the test subset(4.000 instances)

Technique Test Rate
LDA 0.8955
QDA 0.9497
KNN 0.9641
Tree(simple) 0.4799
Bagging 0.9454
Random Forests 0.9915
Boosting 0.5805
SVM 0.9487

Source

David J. Slate

Odesta Corporation;

1890 Maple Ave; Suite 115;

Evanston, IL 60201

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