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Review of Text Recognition Works

by Mohit Agarwal, Baijnath Kaushik
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 137 - Number 5
Year of Publication: 2016
Authors: Mohit Agarwal, Baijnath Kaushik
10.5120/ijca2016908750

Mohit Agarwal, Baijnath Kaushik . Review of Text Recognition Works. International Journal of Computer Applications. 137, 5 ( March 2016), 34-39. DOI=10.5120/ijca2016908750

@article{ 10.5120/ijca2016908750,
author = { Mohit Agarwal, Baijnath Kaushik },
title = { Review of Text Recognition Works },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 137 },
number = { 5 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 34-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume137/number5/24275-2016908750/ },
doi = { 10.5120/ijca2016908750 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:37:36.751208+05:30
%A Mohit Agarwal
%A Baijnath Kaushik
%T Review of Text Recognition Works
%J International Journal of Computer Applications
%@ 0975-8887
%V 137
%N 5
%P 34-39
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Character recognition is an important problem in Pattern Classification. It is difficult to recognize the character in images or pdf or scanned text or handwritten characters scan. There are various ways to recognize text which comprises of Neural Network, Genetic Algorithm, Soft computing and fuzzy logic. In this paper, we compare the various ways already explored by researchers. We have tried to compare the Artificial Neural Network and Genetic Algorithm for the character recognition. The character image is initially segmented into pixel array and then normalized and skew removed with feature extraction to form an input to the neural network or the genetic algorithm. We work with two set of data, training data and test data and the aim is to recognize character correctly in test data.

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Index Terms

Computer Science
Information Sciences

Keywords

text classification text selection digits recognition neural network genetic algorithm ANN GA OCR.

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