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Handwritten Digit Recognition using Slope Detail Features

by A. M. Hafiz, G. M. Bhat
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 93 - Number 5
Year of Publication: 2014
Authors: A. M. Hafiz, G. M. Bhat
10.5120/16210-5512

A. M. Hafiz, G. M. Bhat . Handwritten Digit Recognition using Slope Detail Features. International Journal of Computer Applications. 93, 5 ( May 2014), 14-19. DOI=10.5120/16210-5512

@article{ 10.5120/16210-5512,
author = { A. M. Hafiz, G. M. Bhat },
title = { Handwritten Digit Recognition using Slope Detail Features },
journal = { International Journal of Computer Applications },
issue_date = { May 2014 },
volume = { 93 },
number = { 5 },
month = { May },
year = { 2014 },
issn = { 0975-8887 },
pages = { 14-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume93/number5/16210-5512/ },
doi = { 10.5120/16210-5512 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:15:01.217628+05:30
%A A. M. Hafiz
%A G. M. Bhat
%T Handwritten Digit Recognition using Slope Detail Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 93
%N 5
%P 14-19
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, new features called Slope Detail (SD) features for handwritten digit recognition have been introduced. These features are based on shape analysis of the digit image and extract slant or slope information. They are effective in obtaining good recognition accuracies. When combined with commonly used features, Slope Detail features enhance the digit recognition accuracy. K- Nearest Neighbour (k-NN) and Support Vector Machine (SVM) algorithms have been used for classification purposes. The data sets used are the Semeion Data Set and United States Postal Service (USPS) Data Set. For the USPS Data Set an error rate of 1. 3% was obtained, which has been found to be better than any reported error rate on the said data set.

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Index Terms

Computer Science
Information Sciences

Keywords

Slope Features Handwritten digits Pattern Classification Nearest Neighbor Support Vector Machine Artificial Intelligence Gradient Feature USPS Data Set

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