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Character Recognition using Dynamic Windows

by Mithun Biswas, Ranjan Parekh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Number 15
Year of Publication: 2012
Authors: Mithun Biswas, Ranjan Parekh
10.5120/5620-7912

Mithun Biswas, Ranjan Parekh . Character Recognition using Dynamic Windows. International Journal of Computer Applications. 41, 15 ( March 2012), 47-52. DOI=10.5120/5620-7912

@article{ 10.5120/5620-7912,
author = { Mithun Biswas, Ranjan Parekh },
title = { Character Recognition using Dynamic Windows },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 41 },
number = { 15 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 47-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume41/number15/5620-7912/ },
doi = { 10.5120/5620-7912 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:29:42.137598+05:30
%A Mithun Biswas
%A Ranjan Parekh
%T Character Recognition using Dynamic Windows
%J International Journal of Computer Applications
%@ 0975-8887
%V 41
%N 15
%P 47-52
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper proposes a scheme for recognition of English characters based on features derived from partitioning the character image into non-overlapping cells. A dynamic sliding window moves over each cell and pixel counts obtained from the image portion within the boundaries of the window, contribute towards generation of the feature vector. A total of four passes of the window over the image each with a different window size leads to the generation of a 30-element feature vector. A neural network (multi-layered perceptron) is used for classifying the 26 alphabets of the English language. Accuracies obtained are demonstrated to have been improved upon with respect to contemporary works.

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Index Terms

Computer Science
Information Sciences

Keywords

Dynamic Sliding Window Neural Network Multi-layered Perceptron Feature-vector

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