Research Article

Character Recognition using Dynamic Windows

by  Mithun Biswas, Ranjan Parekh
journal cover
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 41 - Issue 15
Published: March 2012
Authors: Mithun Biswas, Ranjan Parekh
10.5120/5620-7912
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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 },
                        year    = { 2012 },
                        volume  = { 41 },
                        number  = { 15 },
                        pages   = { 47-52 },
                        doi     = { 10.5120/5620-7912 },
                        publisher = { Foundation of Computer Science (FCS), NY, USA }
                        }
                        %0 Journal Article
                        %D 2012
                        %A Mithun Biswas
                        %A Ranjan Parekh
                        %T Character Recognition using Dynamic Windows%T 
                        %J International Journal of Computer Applications
                        %V 41
                        %N 15
                        %P 47-52
                        %R 10.5120/5620-7912
                        %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
No index terms available.
Keywords

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

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